# importing the necessary libraries for analysis and visualizations
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import math
from datetime import datetime
import matplotlib.dates as mdates
from dateutil.relativedelta import relativedelta
from sklearn.preprocessing import MinMaxScaler
import copy
import warnings
warnings.filterwarnings("ignore")
# reading the data to a dataframe
df = pd.read_csv('all_ticks_wide.csv')
# converting to pd datetime
df['timestamp'] = pd.to_datetime(df['timestamp'])
# getting year, month, day as features
df['year'] = df['timestamp'].dt.year
df['month'] = df['timestamp'].dt.month
df['day'] = df['timestamp'].dt.day
df
| timestamp | AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ... | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | year | month | day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012-09-17 06:45:00+00:00 | 22.3978 | 5.2084 | 1.7102 | 3.87 | 1.4683 | 1.1356 | 1.0634 | 6.9909 | 2.9948 | ... | 1.0382 | 3.8620 | 1.90 | 0.4172 | 2.5438 | 2.2619 | 0.7789 | 2012 | 9 | 17 |
| 1 | 2012-09-17 07:00:00+00:00 | 22.3978 | 5.1938 | 1.7066 | 3.86 | 1.4574 | 1.1275 | 1.0634 | 6.9259 | 2.9948 | ... | 1.0382 | 3.8529 | 1.90 | 0.4229 | 2.5266 | 2.2462 | 0.7789 | 2012 | 9 | 17 |
| 2 | 2012-09-17 07:15:00+00:00 | 22.3978 | 5.2084 | 1.7102 | NaN | 1.4610 | 1.1356 | 1.0679 | 6.9909 | 2.9855 | ... | 1.0463 | 3.8436 | 1.91 | 0.4229 | 2.5266 | 2.2566 | 0.7789 | 2012 | 9 | 17 |
| 3 | 2012-09-17 07:30:00+00:00 | 22.3978 | 5.1938 | 1.7102 | 3.86 | 1.4537 | 1.1275 | 1.0679 | 6.9584 | 2.9855 | ... | 1.0382 | 3.8529 | 1.91 | 0.4286 | 2.5324 | 2.2619 | 0.7860 | 2012 | 9 | 17 |
| 4 | 2012-09-17 07:45:00+00:00 | 22.5649 | 5.2084 | 1.7102 | 3.87 | 1.4574 | 1.1356 | 1.0725 | 6.9909 | 2.9760 | ... | 1.0382 | 3.8620 | 1.90 | 0.4286 | 2.5324 | 2.2619 | 0.7789 | 2012 | 9 | 17 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 50007 | 2019-07-23 14:00:00+00:00 | 20.4800 | 7.7300 | 9.1400 | 2.47 | 3.2300 | 1.2100 | 2.8400 | 20.3000 | NaN | ... | 1.0500 | 4.8600 | 9.98 | 5.3500 | 2.7500 | 4.2500 | NaN | 2019 | 7 | 23 |
| 50008 | 2019-07-23 14:15:00+00:00 | 20.5000 | 7.7200 | 9.1400 | 2.47 | 3.2200 | 1.2100 | 2.8400 | 20.3200 | NaN | ... | 1.0500 | 4.8600 | 9.98 | 5.3400 | 2.7500 | 4.2400 | NaN | 2019 | 7 | 23 |
| 50009 | 2019-07-23 14:30:00+00:00 | 20.5000 | 7.7400 | 9.1300 | 2.46 | 3.2300 | 1.2100 | 2.8300 | 20.3400 | NaN | ... | 1.0500 | 4.8600 | 9.96 | 5.3400 | 2.7600 | 4.2400 | NaN | 2019 | 7 | 23 |
| 50010 | 2019-07-23 14:45:00+00:00 | 20.4000 | 7.7000 | 9.1400 | 2.47 | 3.2400 | 1.2100 | 2.8200 | 20.3800 | NaN | ... | 1.0400 | 4.8600 | 9.94 | 5.3400 | 2.7700 | 4.2400 | NaN | 2019 | 7 | 23 |
| 50011 | 2019-07-23 15:00:00+00:00 | 20.4600 | 7.7000 | 9.1400 | 2.47 | 3.2300 | 1.2000 | 2.8300 | 20.3200 | NaN | ... | 1.0500 | 4.8500 | 9.93 | 5.3300 | 2.7700 | 4.2400 | NaN | 2019 | 7 | 23 |
50012 rows × 64 columns
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 50012 entries, 0 to 50011 Data columns (total 64 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 timestamp 50012 non-null datetime64[ns, UTC] 1 AEFES 48131 non-null float64 2 AKBNK 49209 non-null float64 3 AKSA 48594 non-null float64 4 AKSEN 48171 non-null float64 5 ALARK 48335 non-null float64 6 ALBRK 46862 non-null float64 7 ANACM 48165 non-null float64 8 ARCLK 49045 non-null float64 9 ASELS 48803 non-null float64 10 ASUZU 48433 non-null float64 11 AYGAZ 48119 non-null float64 12 BAGFS 48650 non-null float64 13 BANVT 47951 non-null float64 14 BRISA 48937 non-null float64 15 CCOLA 48749 non-null float64 16 CEMAS 46394 non-null float64 17 ECILC 48492 non-null float64 18 EREGL 49173 non-null float64 19 FROTO 48995 non-null float64 20 GARAN 49308 non-null float64 21 GOODY 48961 non-null float64 22 GUBRF 49057 non-null float64 23 HALKB 49071 non-null float64 24 ICBCT 44336 non-null float64 25 ISCTR 49221 non-null float64 26 ISDMR 12227 non-null float64 27 ISFIN 42877 non-null float64 28 ISYAT 43184 non-null float64 29 KAREL 46032 non-null float64 30 KARSN 48527 non-null float64 31 KCHOL 49093 non-null float64 32 KRDMB 47532 non-null float64 33 KRDMD 49161 non-null float64 34 MGROS 48903 non-null float64 35 OTKAR 48785 non-null float64 36 PARSN 45325 non-null float64 37 PETKM 49184 non-null float64 38 PGSUS 45221 non-null float64 39 PRKME 48466 non-null float64 40 SAHOL 49095 non-null float64 41 SASA 47633 non-null float64 42 SISE 49090 non-null float64 43 SKBNK 47270 non-null float64 44 SODA 48276 non-null float64 45 TCELL 49143 non-null float64 46 THYAO 49282 non-null float64 47 TKFEN 48930 non-null float64 48 TOASO 48946 non-null float64 49 TRKCM 48886 non-null float64 50 TSKB 48384 non-null float64 51 TTKOM 49077 non-null float64 52 TUKAS 45929 non-null float64 53 TUPRS 49143 non-null float64 54 USAK 47659 non-null float64 55 VAKBN 49212 non-null float64 56 VESTL 48781 non-null float64 57 YATAS 46055 non-null float64 58 YKBNK 49225 non-null float64 59 YUNSA 45528 non-null float64 60 ZOREN 48807 non-null float64 61 year 50012 non-null int64 62 month 50012 non-null int64 63 day 50012 non-null int64 dtypes: datetime64[ns, UTC](1), float64(60), int64(3) memory usage: 24.4 MB
# function to fill null values
# it takes the
def fill_null_values_with_average(df, timestamp):
filled_df = df.copy()
other_columns = list(filled_df.columns.difference([timestamp]))
for column in other_columns:
# creating a mask for NaN values in the column
null_mask = filled_df[column].isnull()
# finding the first non-null value before each NaN
before = filled_df[column].where(~null_mask).ffill()
# finding the first non-null value after each NaN
after = filled_df[column].where(~null_mask[::-1]).bfill()[::-1]
# taking the average of them
average = (before + after) / 2
# filling the nan
filled_df[column].fillna(average, inplace=True)
return filled_df
# filling the nan
filled_df = fill_null_values_with_average(df, 'timestamp')
filled_df
| timestamp | AEFES | AKBNK | AKSA | AKSEN | ALARK | ALBRK | ANACM | ARCLK | ASELS | ... | USAK | VAKBN | VESTL | YATAS | YKBNK | YUNSA | ZOREN | year | month | day | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012-09-17 06:45:00+00:00 | 22.3978 | 5.2084 | 1.7102 | 3.87 | 1.4683 | 1.1356 | 1.0634 | 6.9909 | 2.9948 | ... | 1.0382 | 3.8620 | 1.90 | 0.4172 | 2.5438 | 2.2619 | 0.7789 | 2012 | 9 | 17 |
| 1 | 2012-09-17 07:00:00+00:00 | 22.3978 | 5.1938 | 1.7066 | 3.86 | 1.4574 | 1.1275 | 1.0634 | 6.9259 | 2.9948 | ... | 1.0382 | 3.8529 | 1.90 | 0.4229 | 2.5266 | 2.2462 | 0.7789 | 2012 | 9 | 17 |
| 2 | 2012-09-17 07:15:00+00:00 | 22.3978 | 5.2084 | 1.7102 | 3.86 | 1.4610 | 1.1356 | 1.0679 | 6.9909 | 2.9855 | ... | 1.0463 | 3.8436 | 1.91 | 0.4229 | 2.5266 | 2.2566 | 0.7789 | 2012 | 9 | 17 |
| 3 | 2012-09-17 07:30:00+00:00 | 22.3978 | 5.1938 | 1.7102 | 3.86 | 1.4537 | 1.1275 | 1.0679 | 6.9584 | 2.9855 | ... | 1.0382 | 3.8529 | 1.91 | 0.4286 | 2.5324 | 2.2619 | 0.7860 | 2012 | 9 | 17 |
| 4 | 2012-09-17 07:45:00+00:00 | 22.5649 | 5.2084 | 1.7102 | 3.87 | 1.4574 | 1.1356 | 1.0725 | 6.9909 | 2.9760 | ... | 1.0382 | 3.8620 | 1.90 | 0.4286 | 2.5324 | 2.2619 | 0.7789 | 2012 | 9 | 17 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 50007 | 2019-07-23 14:00:00+00:00 | 20.4800 | 7.7300 | 9.1400 | 2.47 | 3.2300 | 1.2100 | 2.8400 | 20.3000 | NaN | ... | 1.0500 | 4.8600 | 9.98 | 5.3500 | 2.7500 | 4.2500 | NaN | 2019 | 7 | 23 |
| 50008 | 2019-07-23 14:15:00+00:00 | 20.5000 | 7.7200 | 9.1400 | 2.47 | 3.2200 | 1.2100 | 2.8400 | 20.3200 | NaN | ... | 1.0500 | 4.8600 | 9.98 | 5.3400 | 2.7500 | 4.2400 | NaN | 2019 | 7 | 23 |
| 50009 | 2019-07-23 14:30:00+00:00 | 20.5000 | 7.7400 | 9.1300 | 2.46 | 3.2300 | 1.2100 | 2.8300 | 20.3400 | NaN | ... | 1.0500 | 4.8600 | 9.96 | 5.3400 | 2.7600 | 4.2400 | NaN | 2019 | 7 | 23 |
| 50010 | 2019-07-23 14:45:00+00:00 | 20.4000 | 7.7000 | 9.1400 | 2.47 | 3.2400 | 1.2100 | 2.8200 | 20.3800 | NaN | ... | 1.0400 | 4.8600 | 9.94 | 5.3400 | 2.7700 | 4.2400 | NaN | 2019 | 7 | 23 |
| 50011 | 2019-07-23 15:00:00+00:00 | 20.4600 | 7.7000 | 9.1400 | 2.47 | 3.2300 | 1.2000 | 2.8300 | 20.3200 | NaN | ... | 1.0500 | 4.8500 | 9.93 | 5.3300 | 2.7700 | 4.2400 | NaN | 2019 | 7 | 23 |
50012 rows × 64 columns
# info about number of outliers
filled_df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 50012 entries, 0 to 50011 Data columns (total 64 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 timestamp 50012 non-null datetime64[ns, UTC] 1 AEFES 50012 non-null float64 2 AKBNK 50012 non-null float64 3 AKSA 50012 non-null float64 4 AKSEN 50012 non-null float64 5 ALARK 50012 non-null float64 6 ALBRK 50012 non-null float64 7 ANACM 50012 non-null float64 8 ARCLK 50012 non-null float64 9 ASELS 49982 non-null float64 10 ASUZU 50012 non-null float64 11 AYGAZ 50012 non-null float64 12 BAGFS 50012 non-null float64 13 BANVT 50012 non-null float64 14 BRISA 50012 non-null float64 15 CCOLA 50012 non-null float64 16 CEMAS 50012 non-null float64 17 ECILC 50012 non-null float64 18 EREGL 50012 non-null float64 19 FROTO 50012 non-null float64 20 GARAN 50012 non-null float64 21 GOODY 50012 non-null float64 22 GUBRF 50012 non-null float64 23 HALKB 50012 non-null float64 24 ICBCT 50012 non-null float64 25 ISCTR 50012 non-null float64 26 ISDMR 26057 non-null float64 27 ISFIN 50012 non-null float64 28 ISYAT 50012 non-null float64 29 KAREL 49980 non-null float64 30 KARSN 49980 non-null float64 31 KCHOL 50012 non-null float64 32 KRDMB 50012 non-null float64 33 KRDMD 50012 non-null float64 34 MGROS 50012 non-null float64 35 OTKAR 50012 non-null float64 36 PARSN 50012 non-null float64 37 PETKM 50012 non-null float64 38 PGSUS 46015 non-null float64 39 PRKME 50012 non-null float64 40 SAHOL 50012 non-null float64 41 SASA 49980 non-null float64 42 SISE 49980 non-null float64 43 SKBNK 50012 non-null float64 44 SODA 50012 non-null float64 45 TCELL 50012 non-null float64 46 THYAO 50012 non-null float64 47 TKFEN 50012 non-null float64 48 TOASO 50012 non-null float64 49 TRKCM 49980 non-null float64 50 TSKB 50012 non-null float64 51 TTKOM 50012 non-null float64 52 TUKAS 50012 non-null float64 53 TUPRS 50012 non-null float64 54 USAK 50012 non-null float64 55 VAKBN 50012 non-null float64 56 VESTL 50012 non-null float64 57 YATAS 50012 non-null float64 58 YKBNK 50012 non-null float64 59 YUNSA 50012 non-null float64 60 ZOREN 49980 non-null float64 61 year 50012 non-null int64 62 month 50012 non-null int64 63 day 50012 non-null int64 dtypes: datetime64[ns, UTC](1), float64(60), int64(3) memory usage: 24.4 MB
Now, we will select 6 stock indexes from 3 different sectors, preferably with non-null entries in the interval 15/01/2017 - 15/01/2019 . Let's consider
# copy df
df = filled_df.copy()
# start date
start = pd.to_datetime("2017-01-15 09:30:00+00:00")
# end data
end = pd.to_datetime("2019-01-15 09:30:00+00:00")
# filtering with time
ndf = df[(df['timestamp'] >= start) & (df['timestamp'] <= end)]
# filtering the necessary columns
df = ndf[['timestamp', 'year', 'month', 'AKBNK', 'VAKBN', 'ARCLK', 'TUPRS', 'TCELL', 'THYAO']].copy()
# years and months as array
years = [2017, 2018, 2019]
months = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# the selected stocks
indexes_selected = ['AKBNK', 'VAKBN', 'ARCLK', 'TUPRS', 'TCELL', 'THYAO']
outliers_akbnk = []
outliers_vakbn = []
outliers_arclk = []
outliers_tuprs = []
outliers_tcell = []
outliers_thyao = []
def get_list(index):
if index == "AKBNK":
return outliers_akbnk
elif index == "VAKBN":
return outliers_vakbn
elif index == "ARCLK":
return outliers_arclk
elif index == "TUPRS":
return outliers_tuprs
elif index == "TCELL":
return outliers_tcell
else:
return outliers_thyao
# given a stock in a specific month, constructs the
# control charts for that month
def plot_control_chart(selected):
for index in indexes_selected:
# getting the prices for the stock
prices = selected[index].to_list()
# timestamps
time = selected['timestamp'].to_list()
# mean
mu = np.mean(prices)
# standard deviation
sigma = np.std(prices)
# UCL and LCL
UCL = mu + 3 * sigma
LCL = mu - 3 * sigma
# create a list to store the outliers
outliers = []
# plot Mean, UCL, LCL
plt.figure(figsize=(30, 15))
plt.plot(time, prices, marker='o', label='Data')
plt.axhline(mu, color='r', linestyle='--', label='Mean')
plt.axhline(UCL, color='g', linestyle='--', label='UCL (3-sigma)')
plt.axhline(LCL, color='g', linestyle='--', label='LCL (3-sigma)')
# plot outliers as red dots
for i, price in enumerate(prices):
if price > UCL or price < LCL:
plt.scatter(time[i], price, c='red', marker='o', s=200)
outliers.append((time[i], price))
# plot dates, x axis, y axis, title
date_format = mdates.DateFormatter('%Y-%m-%d %H:%M:%S')
plt.gca().xaxis.set_major_formatter(date_format)
plt.gca().xaxis.set_major_locator(mdates.HourLocator(interval=12))
plt.xlabel('Time', fontsize=26)
plt.ylabel('Price', fontsize=26)
plt.title(f'Control Chart for {index} with 3-sigma Limits\nYear: {selected["year"].iloc[0]}, Month: {selected["month"].iloc[0]}', fontsize=32, fontweight='bold')
plt.legend(fontsize=25)
plt.grid(True)
plt.xticks(rotation=45, fontsize=18)
plt.yticks(fontsize=20)
plt.show()
# print outliers
if outliers:
print(f'Outliers for {index}:')
l = get_list(index)
for outlier in outliers:
l.append((outlier[0],outlier[1]))
print(f'Timestamp: {outlier[0]}, Price: {outlier[1]}')
# group by year, month
grouped = df.groupby(['year', 'month'])
# apply the plot_control_chart function to each group
grouped.apply(plot_control_chart)
Outliers for AKBNK: Timestamp: 2017-01-31 11:30:00+00:00, Price: 6.7643 Timestamp: 2017-01-31 14:15:00+00:00, Price: 6.7643 Timestamp: 2017-01-31 14:30:00+00:00, Price: 6.7803
Outliers for AKBNK: Timestamp: 2017-02-01 08:15:00+00:00, Price: 6.6681 Timestamp: 2017-02-01 08:30:00+00:00, Price: 6.6521
Outliers for VAKBN: Timestamp: 2017-02-01 06:45:00+00:00, Price: 4.8252 Timestamp: 2017-02-01 07:45:00+00:00, Price: 4.8153 Timestamp: 2017-02-01 08:15:00+00:00, Price: 4.8153 Timestamp: 2017-02-01 08:30:00+00:00, Price: 4.8055
Outliers for THYAO: Timestamp: 2017-02-28 12:15:00+00:00, Price: 5.46 Timestamp: 2017-02-28 12:30:00+00:00, Price: 5.45 Timestamp: 2017-02-28 12:45:00+00:00, Price: 5.42 Timestamp: 2017-02-28 13:00:00+00:00, Price: 5.42 Timestamp: 2017-02-28 13:15:00+00:00, Price: 5.44 Timestamp: 2017-02-28 13:30:00+00:00, Price: 5.46 Timestamp: 2017-02-28 13:45:00+00:00, Price: 5.45 Timestamp: 2017-02-28 14:00:00+00:00, Price: 5.46 Timestamp: 2017-02-28 14:15:00+00:00, Price: 5.46 Timestamp: 2017-02-28 14:30:00+00:00, Price: 5.45 Timestamp: 2017-02-28 14:45:00+00:00, Price: 5.45 Timestamp: 2017-02-28 15:00:00+00:00, Price: 5.46
Outliers for AKBNK: Timestamp: 2017-03-06 12:45:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:00:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:15:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:30:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:45:00+00:00, Price: 7.4294 Timestamp: 2017-03-06 14:00:00+00:00, Price: 7.4294 Timestamp: 2017-03-07 06:45:00+00:00, Price: 7.4535 Timestamp: 2017-03-07 07:00:00+00:00, Price: 7.4294 Timestamp: 2017-03-07 07:15:00+00:00, Price: 7.4615 Timestamp: 2017-03-07 07:30:00+00:00, Price: 7.4375 Timestamp: 2017-03-07 07:45:00+00:00, Price: 7.4455
Outliers for VAKBN: Timestamp: 2017-03-06 12:15:00+00:00, Price: 5.4906 Timestamp: 2017-03-06 12:30:00+00:00, Price: 5.5005 Timestamp: 2017-03-06 12:45:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 13:00:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 13:15:00+00:00, Price: 5.52 Timestamp: 2017-03-06 13:30:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 13:45:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 15:00:00+00:00, Price: 5.4906 Timestamp: 2017-03-07 06:45:00+00:00, Price: 5.4906 Timestamp: 2017-03-07 07:15:00+00:00, Price: 5.5005
Outliers for TUPRS: Timestamp: 2017-04-28 14:30:00+00:00, Price: 71.0298 Timestamp: 2017-04-28 14:45:00+00:00, Price: 71.0298 Timestamp: 2017-04-28 15:00:00+00:00, Price: 71.0298
Outliers for ARCLK: Timestamp: 2017-06-01 06:45:00+00:00, Price: 23.7059 Timestamp: 2017-06-01 07:00:00+00:00, Price: 23.7831 Timestamp: 2017-06-01 07:15:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 07:30:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 07:45:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 08:00:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 08:15:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 08:30:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 08:45:00+00:00, Price: 23.6673 Timestamp: 2017-06-01 09:00:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 09:15:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 09:30:00+00:00, Price: 23.7638 Timestamp: 2017-06-01 09:45:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 10:45:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 11:00:00+00:00, Price: 23.7638 Timestamp: 2017-06-01 11:15:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 11:30:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 11:45:00+00:00, Price: 23.7638 Timestamp: 2017-06-01 12:00:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 12:15:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 12:30:00+00:00, Price: 23.7831 Timestamp: 2017-06-01 12:45:00+00:00, Price: 23.7831 Timestamp: 2017-06-01 13:00:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 13:15:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 13:30:00+00:00, Price: 23.6673 Timestamp: 2017-06-01 13:45:00+00:00, Price: 23.7059 Timestamp: 2017-06-01 14:00:00+00:00, Price: 23.648 Timestamp: 2017-06-01 14:15:00+00:00, Price: 23.6673 Timestamp: 2017-06-01 14:30:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 14:45:00+00:00, Price: 23.841
Outliers for TUPRS: Timestamp: 2017-08-01 09:00:00+00:00, Price: 84.2834 Timestamp: 2017-08-01 09:15:00+00:00, Price: 84.7596 Timestamp: 2017-08-01 09:30:00+00:00, Price: 84.7596 Timestamp: 2017-08-01 09:45:00+00:00, Price: 84.6008 Timestamp: 2017-08-01 10:45:00+00:00, Price: 84.6008 Timestamp: 2017-08-01 11:00:00+00:00, Price: 84.5215 Timestamp: 2017-08-01 12:15:00+00:00, Price: 84.6008 Timestamp: 2017-08-01 12:30:00+00:00, Price: 84.4421 Timestamp: 2017-08-01 12:45:00+00:00, Price: 84.204 Timestamp: 2017-08-01 13:00:00+00:00, Price: 84.3627 Timestamp: 2017-08-01 13:15:00+00:00, Price: 84.4421 Timestamp: 2017-08-01 13:30:00+00:00, Price: 84.2834 Timestamp: 2017-08-01 13:45:00+00:00, Price: 84.204 Timestamp: 2017-08-01 14:00:00+00:00, Price: 84.0453 Timestamp: 2017-08-01 14:15:00+00:00, Price: 84.0453 Timestamp: 2017-08-01 14:30:00+00:00, Price: 84.1246 Timestamp: 2017-08-02 06:45:00+00:00, Price: 84.7596
Outliers for AKBNK: Timestamp: 2017-10-09 07:00:00+00:00, Price: 7.3946 Timestamp: 2017-10-09 07:30:00+00:00, Price: 7.4357 Timestamp: 2017-10-09 07:45:00+00:00, Price: 7.4439 Timestamp: 2017-10-09 08:00:00+00:00, Price: 7.4357 Timestamp: 2017-10-09 08:15:00+00:00, Price: 7.4192 Timestamp: 2017-10-09 08:30:00+00:00, Price: 7.4275 Timestamp: 2017-10-09 08:45:00+00:00, Price: 7.4192 Timestamp: 2017-10-09 09:00:00+00:00, Price: 7.4275 Timestamp: 2017-10-09 09:15:00+00:00, Price: 7.4357
Outliers for VAKBN: Timestamp: 2017-10-09 07:30:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 07:45:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 08:15:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 08:30:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 08:45:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 09:00:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 09:15:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 09:30:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 10:45:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 11:00:00+00:00, Price: 5.8364 Timestamp: 2017-10-20 07:45:00+00:00, Price: 6.5068 Timestamp: 2017-10-20 08:00:00+00:00, Price: 6.5265 Timestamp: 2017-10-20 08:15:00+00:00, Price: 6.5068 Timestamp: 2017-10-20 08:45:00+00:00, Price: 6.4969 Timestamp: 2017-10-20 09:15:00+00:00, Price: 6.4969
Outliers for TCELL: Timestamp: 2017-11-02 06:45:00+00:00, Price: 13.6104 Timestamp: 2017-11-02 07:00:00+00:00, Price: 13.7186 Timestamp: 2017-11-02 07:15:00+00:00, Price: 13.6014 Timestamp: 2017-11-02 07:30:00+00:00, Price: 13.6014 Timestamp: 2017-11-02 07:45:00+00:00, Price: 13.6374 Timestamp: 2017-11-02 08:00:00+00:00, Price: 13.5563 Timestamp: 2017-11-02 10:45:00+00:00, Price: 13.5384 Timestamp: 2017-11-30 12:30:00+00:00, Price: 13.5294 Timestamp: 2017-11-30 13:15:00+00:00, Price: 13.5834 Timestamp: 2017-11-30 13:30:00+00:00, Price: 13.5384 Timestamp: 2017-11-30 13:45:00+00:00, Price: 13.5834 Timestamp: 2017-11-30 14:00:00+00:00, Price: 13.6824 Timestamp: 2017-11-30 14:15:00+00:00, Price: 13.7095 Timestamp: 2017-11-30 14:30:00+00:00, Price: 13.6284 Timestamp: 2017-11-30 14:45:00+00:00, Price: 13.6555 Timestamp: 2017-11-30 15:00:00+00:00, Price: 13.7005
Outliers for ARCLK: Timestamp: 2017-12-01 06:45:00+00:00, Price: 19.1114 Timestamp: 2017-12-29 13:45:00+00:00, Price: 20.5013 Timestamp: 2017-12-29 14:15:00+00:00, Price: 20.5013 Timestamp: 2017-12-29 14:30:00+00:00, Price: 20.6751 Timestamp: 2017-12-29 14:45:00+00:00, Price: 20.7137 Timestamp: 2017-12-29 15:00:00+00:00, Price: 20.7716
Outliers for VAKBN: Timestamp: 2018-02-01 07:30:00+00:00, Price: 7.5222 Timestamp: 2018-02-01 07:45:00+00:00, Price: 7.5321
Outliers for TCELL: Timestamp: 2018-02-20 14:45:00+00:00, Price: 13.5483 Timestamp: 2018-02-20 15:00:00+00:00, Price: 13.5576
Outliers for VAKBN: Timestamp: 2018-04-30 14:30:00+00:00, Price: 5.8857 Timestamp: 2018-04-30 14:45:00+00:00, Price: 5.8955 Timestamp: 2018-04-30 15:00:00+00:00, Price: 5.8857
Outliers for ARCLK: Timestamp: 2018-05-02 07:15:00+00:00, Price: 18.67 Timestamp: 2018-05-02 07:30:00+00:00, Price: 18.7 Timestamp: 2018-05-02 07:45:00+00:00, Price: 18.57 Timestamp: 2018-05-02 08:00:00+00:00, Price: 18.65 Timestamp: 2018-05-02 08:15:00+00:00, Price: 18.57
Outliers for TUPRS: Timestamp: 2018-05-10 07:45:00+00:00, Price: 83.5047 Timestamp: 2018-05-10 08:00:00+00:00, Price: 83.5047
Outliers for TCELL: Timestamp: 2018-05-02 06:45:00+00:00, Price: 12.9823 Timestamp: 2018-05-02 07:00:00+00:00, Price: 12.8987 Timestamp: 2018-05-02 07:15:00+00:00, Price: 12.9173 Timestamp: 2018-05-02 08:15:00+00:00, Price: 12.908 Timestamp: 2018-05-31 14:15:00+00:00, Price: 11.1821 Timestamp: 2018-05-31 15:00:00+00:00, Price: 11.1356
Outliers for AKBNK: Timestamp: 2018-06-01 06:45:00+00:00, Price: 6.8976
Outliers for THYAO: Timestamp: 2018-08-17 07:15:00+00:00, Price: 14.65 Timestamp: 2018-08-17 07:30:00+00:00, Price: 14.35 Timestamp: 2018-08-17 07:45:00+00:00, Price: 14.48 Timestamp: 2018-08-17 08:15:00+00:00, Price: 14.67 Timestamp: 2018-08-17 08:30:00+00:00, Price: 14.62 Timestamp: 2018-08-17 08:45:00+00:00, Price: 14.57 Timestamp: 2018-08-17 09:00:00+00:00, Price: 14.6 Timestamp: 2018-08-17 09:15:00+00:00, Price: 14.52 Timestamp: 2018-08-17 09:30:00+00:00, Price: 14.58 Timestamp: 2018-08-17 09:45:00+00:00, Price: 14.57 Timestamp: 2018-08-17 10:00:00+00:00, Price: 14.58 Timestamp: 2018-08-17 10:45:00+00:00, Price: 14.59
Outliers for AKBNK: Timestamp: 2018-11-01 06:45:00+00:00, Price: 5.6451 Timestamp: 2018-11-01 07:00:00+00:00, Price: 5.6365 Timestamp: 2018-11-01 07:15:00+00:00, Price: 5.5679 Timestamp: 2018-11-01 07:30:00+00:00, Price: 5.5507 Timestamp: 2018-11-01 07:45:00+00:00, Price: 5.5679 Timestamp: 2018-11-01 08:00:00+00:00, Price: 5.6193 Timestamp: 2018-11-01 08:15:00+00:00, Price: 5.6365 Timestamp: 2018-11-01 08:30:00+00:00, Price: 5.6536 Timestamp: 2018-11-01 08:45:00+00:00, Price: 5.7309 Timestamp: 2018-11-01 09:00:00+00:00, Price: 5.7223 Timestamp: 2018-11-01 09:15:00+00:00, Price: 5.7309 Timestamp: 2018-11-01 09:30:00+00:00, Price: 5.7394 Timestamp: 2018-11-01 11:15:00+00:00, Price: 5.7394 Timestamp: 2018-11-01 12:30:00+00:00, Price: 5.7309
Outliers for AKBNK: Timestamp: 2018-12-03 06:45:00+00:00, Price: 6.6831 Timestamp: 2018-12-03 07:00:00+00:00, Price: 6.7003 Timestamp: 2018-12-03 07:15:00+00:00, Price: 6.6831 Timestamp: 2018-12-03 07:30:00+00:00, Price: 6.6917 Timestamp: 2018-12-03 07:45:00+00:00, Price: 6.6746 Timestamp: 2018-12-03 08:00:00+00:00, Price: 6.7089 Timestamp: 2018-12-03 08:15:00+00:00, Price: 6.7175 Timestamp: 2018-12-03 08:30:00+00:00, Price: 6.6746 Timestamp: 2018-12-03 08:45:00+00:00, Price: 6.6574 Timestamp: 2018-12-03 09:00:00+00:00, Price: 6.6402 Timestamp: 2018-12-03 09:15:00+00:00, Price: 6.6488 Timestamp: 2018-12-03 09:30:00+00:00, Price: 6.6574 Timestamp: 2018-12-03 09:45:00+00:00, Price: 6.6488 Timestamp: 2018-12-03 10:45:00+00:00, Price: 6.6317
# box plot
def plot_box_plot(selected):
# getting year and month info from df
year = selected["year"].iloc[0]
month = selected["month"].iloc[0]
# looping over stocks
for index in indexes_selected:
# initializing the plot, title, x & y axis
plt.figure(figsize=(30, 15))
ax = sns.boxplot(data=selected, x='month', y=index)
plt.title(str(index) + " " + f'Stock Price Boxplot for {year}/{month}', fontsize=30, fontweight='bold')
plt.xlabel('Month', fontsize=25)
plt.ylabel('Stock Price', fontsize=25)
# identifying and marking both upper and lower outliers
threshold = 1.5 # Adjust this threshold as needed
upper_outliers = selected[selected[index] > (selected[index].quantile(0.75) + threshold * (selected[index].quantile(0.75) - selected[index].quantile(0.25)))]
lower_outliers = selected[selected[index] < (selected[index].quantile(0.25) - threshold * (selected[index].quantile(0.75) - selected[index].quantile(0.25)))]
# printing information about the outliers
if not upper_outliers.empty:
print('Upper Outliers for ' + str(index) + " in " + str(year) + "/" + str(month))
for _, row in upper_outliers.iterrows():
timestamp = row['timestamp']
price = row[index]
print(f'Timestamp: {timestamp}, Price: {price}')
if not lower_outliers.empty:
print('Lower Outliers for ' + str(index) + " in " + str(year) + "/" + str(month))
for _, row in lower_outliers.iterrows():
timestamp = row['timestamp']
price = row[index]
print(f'Timestamp: {timestamp}, Price: {price}')
if lower_outliers.empty and upper_outliers.empty:
print("NO OUTLIERS")
plt.show()
# group by year, month
grouped = df.groupby(['year', 'month'])
# apply the plot_box_plot function to each group
grouped.apply(plot_box_plot)
Upper Outliers for AKBNK in 2017/1 Timestamp: 2017-01-30 10:45:00+00:00, Price: 6.5238 Timestamp: 2017-01-30 11:00:00+00:00, Price: 6.5478 Timestamp: 2017-01-30 11:15:00+00:00, Price: 6.572 Timestamp: 2017-01-30 11:30:00+00:00, Price: 6.5639 Timestamp: 2017-01-30 11:45:00+00:00, Price: 6.5559 Timestamp: 2017-01-30 12:00:00+00:00, Price: 6.572 Timestamp: 2017-01-30 12:15:00+00:00, Price: 6.5799 Timestamp: 2017-01-30 12:30:00+00:00, Price: 6.5799 Timestamp: 2017-01-30 12:45:00+00:00, Price: 6.572 Timestamp: 2017-01-30 13:00:00+00:00, Price: 6.596 Timestamp: 2017-01-30 13:15:00+00:00, Price: 6.612 Timestamp: 2017-01-30 13:30:00+00:00, Price: 6.604 Timestamp: 2017-01-30 13:45:00+00:00, Price: 6.6281 Timestamp: 2017-01-30 14:00:00+00:00, Price: 6.6441 Timestamp: 2017-01-30 14:15:00+00:00, Price: 6.6681 Timestamp: 2017-01-30 14:30:00+00:00, Price: 6.6842 Timestamp: 2017-01-30 14:45:00+00:00, Price: 6.6842 Timestamp: 2017-01-30 15:00:00+00:00, Price: 6.6842 Timestamp: 2017-01-31 06:45:00+00:00, Price: 6.6761 Timestamp: 2017-01-31 07:00:00+00:00, Price: 6.6921 Timestamp: 2017-01-31 07:15:00+00:00, Price: 6.6921 Timestamp: 2017-01-31 07:30:00+00:00, Price: 6.6921 Timestamp: 2017-01-31 07:45:00+00:00, Price: 6.6681 Timestamp: 2017-01-31 08:00:00+00:00, Price: 6.6681 Timestamp: 2017-01-31 08:15:00+00:00, Price: 6.6681 Timestamp: 2017-01-31 08:30:00+00:00, Price: 6.6681 Timestamp: 2017-01-31 08:45:00+00:00, Price: 6.6761 Timestamp: 2017-01-31 09:00:00+00:00, Price: 6.6761 Timestamp: 2017-01-31 09:15:00+00:00, Price: 6.6842 Timestamp: 2017-01-31 09:30:00+00:00, Price: 6.7082 Timestamp: 2017-01-31 09:45:00+00:00, Price: 6.7163 Timestamp: 2017-01-31 10:45:00+00:00, Price: 6.7002 Timestamp: 2017-01-31 11:00:00+00:00, Price: 6.7403 Timestamp: 2017-01-31 11:15:00+00:00, Price: 6.7563 Timestamp: 2017-01-31 11:30:00+00:00, Price: 6.7643 Timestamp: 2017-01-31 11:45:00+00:00, Price: 6.6921 Timestamp: 2017-01-31 12:00:00+00:00, Price: 6.6921 Timestamp: 2017-01-31 12:15:00+00:00, Price: 6.7082 Timestamp: 2017-01-31 12:30:00+00:00, Price: 6.6842 Timestamp: 2017-01-31 12:45:00+00:00, Price: 6.6842 Timestamp: 2017-01-31 13:00:00+00:00, Price: 6.6921 Timestamp: 2017-01-31 13:15:00+00:00, Price: 6.6761 Timestamp: 2017-01-31 13:30:00+00:00, Price: 6.6681 Timestamp: 2017-01-31 13:45:00+00:00, Price: 6.7322 Timestamp: 2017-01-31 14:00:00+00:00, Price: 6.7163 Timestamp: 2017-01-31 14:15:00+00:00, Price: 6.7643 Timestamp: 2017-01-31 14:30:00+00:00, Price: 6.7803 Timestamp: 2017-01-31 14:45:00+00:00, Price: 6.7563 Timestamp: 2017-01-31 15:00:00+00:00, Price: 6.7322
Upper Outliers for VAKBN in 2017/1 Timestamp: 2017-01-30 08:15:00+00:00, Price: 4.7469 Timestamp: 2017-01-30 08:30:00+00:00, Price: 4.7469 Timestamp: 2017-01-30 08:45:00+00:00, Price: 4.786 Timestamp: 2017-01-30 09:00:00+00:00, Price: 4.8153 Timestamp: 2017-01-30 09:15:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 09:30:00+00:00, Price: 4.8153 Timestamp: 2017-01-30 09:45:00+00:00, Price: 4.8153 Timestamp: 2017-01-30 10:45:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 11:00:00+00:00, Price: 4.8055 Timestamp: 2017-01-30 11:15:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 11:30:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 11:45:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 12:00:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 12:15:00+00:00, Price: 4.8349 Timestamp: 2017-01-30 12:30:00+00:00, Price: 4.8252 Timestamp: 2017-01-30 12:45:00+00:00, Price: 4.8153 Timestamp: 2017-01-30 13:00:00+00:00, Price: 4.8349 Timestamp: 2017-01-30 13:15:00+00:00, Price: 4.8545 Timestamp: 2017-01-30 13:30:00+00:00, Price: 4.8447 Timestamp: 2017-01-30 13:45:00+00:00, Price: 4.8447 Timestamp: 2017-01-30 14:00:00+00:00, Price: 4.8545 Timestamp: 2017-01-30 14:15:00+00:00, Price: 4.8545 Timestamp: 2017-01-30 14:30:00+00:00, Price: 4.8545 Timestamp: 2017-01-30 14:45:00+00:00, Price: 4.8643 Timestamp: 2017-01-30 15:00:00+00:00, Price: 4.8643 Timestamp: 2017-01-31 06:45:00+00:00, Price: 4.8741 Timestamp: 2017-01-31 07:00:00+00:00, Price: 4.8545 Timestamp: 2017-01-31 07:15:00+00:00, Price: 4.8349 Timestamp: 2017-01-31 07:30:00+00:00, Price: 4.8545 Timestamp: 2017-01-31 07:45:00+00:00, Price: 4.8252 Timestamp: 2017-01-31 08:00:00+00:00, Price: 4.7958 Timestamp: 2017-01-31 08:15:00+00:00, Price: 4.8153 Timestamp: 2017-01-31 08:30:00+00:00, Price: 4.8252 Timestamp: 2017-01-31 08:45:00+00:00, Price: 4.8643 Timestamp: 2017-01-31 09:00:00+00:00, Price: 4.8741 Timestamp: 2017-01-31 09:15:00+00:00, Price: 4.8741 Timestamp: 2017-01-31 09:30:00+00:00, Price: 4.8937 Timestamp: 2017-01-31 09:45:00+00:00, Price: 4.8838 Timestamp: 2017-01-31 10:45:00+00:00, Price: 4.8838 Timestamp: 2017-01-31 11:00:00+00:00, Price: 4.9034 Timestamp: 2017-01-31 11:15:00+00:00, Price: 4.8937 Timestamp: 2017-01-31 11:30:00+00:00, Price: 4.9034 Timestamp: 2017-01-31 11:45:00+00:00, Price: 4.8643 Timestamp: 2017-01-31 12:00:00+00:00, Price: 4.8545 Timestamp: 2017-01-31 12:15:00+00:00, Price: 4.8643 Timestamp: 2017-01-31 12:30:00+00:00, Price: 4.8252 Timestamp: 2017-01-31 12:45:00+00:00, Price: 4.8252 Timestamp: 2017-01-31 13:00:00+00:00, Price: 4.8349 Timestamp: 2017-01-31 13:15:00+00:00, Price: 4.8153 Timestamp: 2017-01-31 13:30:00+00:00, Price: 4.8252 Timestamp: 2017-01-31 13:45:00+00:00, Price: 4.8349 Timestamp: 2017-01-31 14:00:00+00:00, Price: 4.8349 Timestamp: 2017-01-31 14:15:00+00:00, Price: 4.8643 Timestamp: 2017-01-31 14:30:00+00:00, Price: 4.8349 Timestamp: 2017-01-31 14:45:00+00:00, Price: 4.8153 Timestamp: 2017-01-31 15:00:00+00:00, Price: 4.8055 Lower Outliers for VAKBN in 2017/1 Timestamp: 2017-01-16 06:45:00+00:00, Price: 4.4434 Timestamp: 2017-01-16 14:45:00+00:00, Price: 4.4337 Timestamp: 2017-01-16 15:00:00+00:00, Price: 4.4238 Timestamp: 2017-01-17 07:00:00+00:00, Price: 4.4337 Timestamp: 2017-01-17 08:45:00+00:00, Price: 4.4434
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for AKBNK in 2017/2 Timestamp: 2017-02-01 06:45:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 07:00:00+00:00, Price: 6.7403 Timestamp: 2017-02-01 07:15:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 07:30:00+00:00, Price: 6.7242 Timestamp: 2017-02-01 07:45:00+00:00, Price: 6.6842 Timestamp: 2017-02-01 08:00:00+00:00, Price: 6.7082 Timestamp: 2017-02-01 08:15:00+00:00, Price: 6.6681 Timestamp: 2017-02-01 08:30:00+00:00, Price: 6.6521 Timestamp: 2017-02-01 08:45:00+00:00, Price: 6.6842 Timestamp: 2017-02-01 09:00:00+00:00, Price: 6.7082 Timestamp: 2017-02-01 09:15:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 09:30:00+00:00, Price: 6.7082 Timestamp: 2017-02-01 09:45:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 10:00:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 10:45:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 11:00:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 11:15:00+00:00, Price: 6.7163 Timestamp: 2017-02-01 11:30:00+00:00, Price: 6.7242 Timestamp: 2017-02-01 11:45:00+00:00, Price: 6.7322 Timestamp: 2017-02-01 12:00:00+00:00, Price: 6.7242 Timestamp: 2017-02-01 12:15:00+00:00, Price: 6.7322 Timestamp: 2017-02-01 12:30:00+00:00, Price: 6.7322 Timestamp: 2017-02-01 12:45:00+00:00, Price: 6.7322 Timestamp: 2017-02-01 13:00:00+00:00, Price: 6.7322 Timestamp: 2017-02-01 13:15:00+00:00, Price: 6.7242 Timestamp: 2017-02-01 13:30:00+00:00, Price: 6.7242 Timestamp: 2017-02-01 13:45:00+00:00, Price: 6.7242 Timestamp: 2017-02-01 14:00:00+00:00, Price: 6.7483 Timestamp: 2017-02-01 14:30:00+00:00, Price: 6.7643 Timestamp: 2017-02-01 14:45:00+00:00, Price: 6.7643 Timestamp: 2017-02-01 15:00:00+00:00, Price: 6.7724
Lower Outliers for VAKBN in 2017/2 Timestamp: 2017-02-01 06:45:00+00:00, Price: 4.8252 Timestamp: 2017-02-01 07:00:00+00:00, Price: 4.8545 Timestamp: 2017-02-01 07:15:00+00:00, Price: 4.8349 Timestamp: 2017-02-01 07:30:00+00:00, Price: 4.8349 Timestamp: 2017-02-01 07:45:00+00:00, Price: 4.8153 Timestamp: 2017-02-01 08:00:00+00:00, Price: 4.8349 Timestamp: 2017-02-01 08:15:00+00:00, Price: 4.8153 Timestamp: 2017-02-01 08:30:00+00:00, Price: 4.8055 Timestamp: 2017-02-01 08:45:00+00:00, Price: 4.8349 Timestamp: 2017-02-01 09:00:00+00:00, Price: 4.8447 Timestamp: 2017-02-01 09:15:00+00:00, Price: 4.8447 Timestamp: 2017-02-01 09:30:00+00:00, Price: 4.8447
Upper Outliers for ARCLK in 2017/2 Timestamp: 2017-02-01 08:00:00+00:00, Price: 21.9405 Timestamp: 2017-02-01 08:15:00+00:00, Price: 21.9029 Timestamp: 2017-02-01 08:30:00+00:00, Price: 22.1282 Timestamp: 2017-02-01 08:45:00+00:00, Price: 22.0344 Timestamp: 2017-02-01 09:00:00+00:00, Price: 21.978 Timestamp: 2017-02-01 09:15:00+00:00, Price: 21.978 Timestamp: 2017-02-01 09:30:00+00:00, Price: 22.0344 Timestamp: 2017-02-01 09:45:00+00:00, Price: 22.1094 Timestamp: 2017-02-01 10:00:00+00:00, Price: 22.053150000000002 Timestamp: 2017-02-01 10:45:00+00:00, Price: 21.9969 Timestamp: 2017-02-01 11:00:00+00:00, Price: 22.0344 Timestamp: 2017-02-01 11:15:00+00:00, Price: 22.0719 Timestamp: 2017-02-01 11:30:00+00:00, Price: 22.0344 Timestamp: 2017-02-01 11:45:00+00:00, Price: 22.0907 Timestamp: 2017-02-01 12:00:00+00:00, Price: 22.1282 Timestamp: 2017-02-01 12:15:00+00:00, Price: 22.0531 Timestamp: 2017-02-01 12:30:00+00:00, Price: 22.0156 Timestamp: 2017-02-01 12:45:00+00:00, Price: 22.0156 Timestamp: 2017-02-01 13:00:00+00:00, Price: 22.0344 Timestamp: 2017-02-01 13:15:00+00:00, Price: 21.978 Timestamp: 2017-02-01 13:30:00+00:00, Price: 21.9969 Timestamp: 2017-02-01 13:45:00+00:00, Price: 22.0344 Timestamp: 2017-02-01 14:00:00+00:00, Price: 21.9593 Timestamp: 2017-02-01 14:15:00+00:00, Price: 21.978 Timestamp: 2017-02-01 14:30:00+00:00, Price: 21.978 Timestamp: 2017-02-01 14:45:00+00:00, Price: 22.0156 Timestamp: 2017-02-01 15:00:00+00:00, Price: 22.1094 Timestamp: 2017-02-02 06:45:00+00:00, Price: 22.1282 Timestamp: 2017-02-02 07:00:00+00:00, Price: 22.0531 Timestamp: 2017-02-02 07:15:00+00:00, Price: 22.3159 Timestamp: 2017-02-02 07:30:00+00:00, Price: 22.466 Timestamp: 2017-02-02 07:45:00+00:00, Price: 22.4472 Timestamp: 2017-02-02 08:00:00+00:00, Price: 22.0719 Timestamp: 2017-02-02 08:15:00+00:00, Price: 22.0907 Timestamp: 2017-02-02 08:30:00+00:00, Price: 22.0344 Timestamp: 2017-02-02 08:45:00+00:00, Price: 22.0344 Timestamp: 2017-02-02 09:00:00+00:00, Price: 22.0156 Timestamp: 2017-02-02 09:15:00+00:00, Price: 21.9593 Timestamp: 2017-02-02 09:30:00+00:00, Price: 21.9969 Timestamp: 2017-02-02 09:45:00+00:00, Price: 21.9593 Timestamp: 2017-02-02 10:45:00+00:00, Price: 21.9405 Timestamp: 2017-02-03 06:45:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 07:00:00+00:00, Price: 22.3722 Timestamp: 2017-02-03 07:15:00+00:00, Price: 22.4285 Timestamp: 2017-02-03 07:30:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 07:45:00+00:00, Price: 22.2783 Timestamp: 2017-02-03 08:00:00+00:00, Price: 22.2783 Timestamp: 2017-02-03 08:15:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 08:30:00+00:00, Price: 22.3722 Timestamp: 2017-02-03 08:45:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 09:00:00+00:00, Price: 22.2596 Timestamp: 2017-02-03 09:15:00+00:00, Price: 22.2783 Timestamp: 2017-02-03 09:30:00+00:00, Price: 22.2971 Timestamp: 2017-02-03 09:45:00+00:00, Price: 22.2596 Timestamp: 2017-02-03 10:45:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 11:00:00+00:00, Price: 22.4098 Timestamp: 2017-02-03 11:15:00+00:00, Price: 22.3534 Timestamp: 2017-02-03 11:30:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 11:45:00+00:00, Price: 22.3722 Timestamp: 2017-02-03 12:00:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 12:15:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 12:30:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 12:45:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 13:00:00+00:00, Price: 22.3722 Timestamp: 2017-02-03 13:15:00+00:00, Price: 22.2971 Timestamp: 2017-02-03 13:30:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 13:45:00+00:00, Price: 22.3159 Timestamp: 2017-02-03 14:00:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 14:15:00+00:00, Price: 22.3722 Timestamp: 2017-02-03 14:30:00+00:00, Price: 22.3347 Timestamp: 2017-02-03 14:45:00+00:00, Price: 22.2408 Timestamp: 2017-02-03 15:00:00+00:00, Price: 22.3347 Timestamp: 2017-02-06 06:45:00+00:00, Price: 22.5223 Timestamp: 2017-02-06 07:00:00+00:00, Price: 22.5599 Timestamp: 2017-02-06 07:15:00+00:00, Price: 22.5411 Timestamp: 2017-02-06 07:30:00+00:00, Price: 22.6161 Timestamp: 2017-02-06 07:45:00+00:00, Price: 22.4848 Timestamp: 2017-02-06 08:00:00+00:00, Price: 22.3159 Timestamp: 2017-02-06 08:15:00+00:00, Price: 22.2408 Timestamp: 2017-02-06 08:30:00+00:00, Price: 22.1845 Timestamp: 2017-02-06 08:45:00+00:00, Price: 22.1658 Timestamp: 2017-02-06 09:00:00+00:00, Price: 22.1845 Timestamp: 2017-02-06 09:15:00+00:00, Price: 22.2033 Timestamp: 2017-02-06 09:30:00+00:00, Price: 22.1469 Timestamp: 2017-02-06 09:45:00+00:00, Price: 22.1469 Timestamp: 2017-02-06 10:45:00+00:00, Price: 22.1094 Timestamp: 2017-02-06 11:00:00+00:00, Price: 22.1845 Timestamp: 2017-02-06 11:15:00+00:00, Price: 22.1469 Timestamp: 2017-02-06 11:30:00+00:00, Price: 22.1282 Timestamp: 2017-02-06 11:45:00+00:00, Price: 22.1658 Timestamp: 2017-02-06 12:00:00+00:00, Price: 22.222 Timestamp: 2017-02-06 12:15:00+00:00, Price: 22.2033 Timestamp: 2017-02-06 12:30:00+00:00, Price: 22.2033 Timestamp: 2017-02-06 12:45:00+00:00, Price: 22.2783 Timestamp: 2017-02-06 13:00:00+00:00, Price: 22.2971 Timestamp: 2017-02-06 13:15:00+00:00, Price: 22.2596 Timestamp: 2017-02-06 13:30:00+00:00, Price: 22.1469 Timestamp: 2017-02-06 13:45:00+00:00, Price: 22.0719 Timestamp: 2017-02-06 14:00:00+00:00, Price: 21.9969 Timestamp: 2017-02-06 14:15:00+00:00, Price: 22.0344 Timestamp: 2017-02-06 14:30:00+00:00, Price: 22.0719 Timestamp: 2017-02-06 14:45:00+00:00, Price: 22.0531 Timestamp: 2017-02-06 15:00:00+00:00, Price: 22.0531
Upper Outliers for TUPRS in 2017/2 Timestamp: 2017-02-14 06:45:00+00:00, Price: 64.8258
NO OUTLIERS
Lower Outliers for THYAO in 2017/2 Timestamp: 2017-02-01 08:30:00+00:00, Price: 5.51 Timestamp: 2017-02-28 07:45:00+00:00, Price: 5.52 Timestamp: 2017-02-28 12:00:00+00:00, Price: 5.48 Timestamp: 2017-02-28 12:15:00+00:00, Price: 5.46 Timestamp: 2017-02-28 12:30:00+00:00, Price: 5.45 Timestamp: 2017-02-28 12:45:00+00:00, Price: 5.42 Timestamp: 2017-02-28 13:00:00+00:00, Price: 5.42 Timestamp: 2017-02-28 13:15:00+00:00, Price: 5.44 Timestamp: 2017-02-28 13:30:00+00:00, Price: 5.46 Timestamp: 2017-02-28 13:45:00+00:00, Price: 5.45 Timestamp: 2017-02-28 14:00:00+00:00, Price: 5.46 Timestamp: 2017-02-28 14:15:00+00:00, Price: 5.46 Timestamp: 2017-02-28 14:30:00+00:00, Price: 5.45 Timestamp: 2017-02-28 14:45:00+00:00, Price: 5.45 Timestamp: 2017-02-28 15:00:00+00:00, Price: 5.46
Upper Outliers for AKBNK in 2017/3 Timestamp: 2017-03-06 08:15:00+00:00, Price: 7.3414 Timestamp: 2017-03-06 08:30:00+00:00, Price: 7.3894 Timestamp: 2017-03-06 08:45:00+00:00, Price: 7.3733 Timestamp: 2017-03-06 09:00:00+00:00, Price: 7.3894 Timestamp: 2017-03-06 09:15:00+00:00, Price: 7.3894 Timestamp: 2017-03-06 09:30:00+00:00, Price: 7.3814 Timestamp: 2017-03-06 09:45:00+00:00, Price: 7.3894 Timestamp: 2017-03-06 10:00:00+00:00, Price: 7.3934 Timestamp: 2017-03-06 10:45:00+00:00, Price: 7.3974 Timestamp: 2017-03-06 11:00:00+00:00, Price: 7.3894 Timestamp: 2017-03-06 11:15:00+00:00, Price: 7.4135 Timestamp: 2017-03-06 11:30:00+00:00, Price: 7.4054 Timestamp: 2017-03-06 11:45:00+00:00, Price: 7.3974 Timestamp: 2017-03-06 12:00:00+00:00, Price: 7.3974 Timestamp: 2017-03-06 12:15:00+00:00, Price: 7.4135 Timestamp: 2017-03-06 12:30:00+00:00, Price: 7.4135 Timestamp: 2017-03-06 12:45:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:00:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:15:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:30:00+00:00, Price: 7.4375 Timestamp: 2017-03-06 13:45:00+00:00, Price: 7.4294 Timestamp: 2017-03-06 14:00:00+00:00, Price: 7.4294 Timestamp: 2017-03-06 14:15:00+00:00, Price: 7.4215 Timestamp: 2017-03-06 14:30:00+00:00, Price: 7.3974 Timestamp: 2017-03-06 14:45:00+00:00, Price: 7.4135 Timestamp: 2017-03-06 15:00:00+00:00, Price: 7.4215 Timestamp: 2017-03-07 06:45:00+00:00, Price: 7.4535 Timestamp: 2017-03-07 07:00:00+00:00, Price: 7.4294 Timestamp: 2017-03-07 07:15:00+00:00, Price: 7.4615 Timestamp: 2017-03-07 07:30:00+00:00, Price: 7.4375 Timestamp: 2017-03-07 07:45:00+00:00, Price: 7.4455 Timestamp: 2017-03-07 08:00:00+00:00, Price: 7.4135 Timestamp: 2017-03-07 08:15:00+00:00, Price: 7.3974 Timestamp: 2017-03-07 08:30:00+00:00, Price: 7.3974 Timestamp: 2017-03-07 08:45:00+00:00, Price: 7.3974 Timestamp: 2017-03-07 09:00:00+00:00, Price: 7.4054 Timestamp: 2017-03-07 09:15:00+00:00, Price: 7.4135 Timestamp: 2017-03-07 09:30:00+00:00, Price: 7.4135 Timestamp: 2017-03-07 09:45:00+00:00, Price: 7.4054 Timestamp: 2017-03-07 10:45:00+00:00, Price: 7.4135 Timestamp: 2017-03-07 11:00:00+00:00, Price: 7.4135 Timestamp: 2017-03-07 11:15:00+00:00, Price: 7.3974 Timestamp: 2017-03-07 11:30:00+00:00, Price: 7.3654 Timestamp: 2017-03-07 11:45:00+00:00, Price: 7.3414 Timestamp: 2017-03-07 12:00:00+00:00, Price: 7.3414 Timestamp: 2017-03-07 12:15:00+00:00, Price: 7.3414
Upper Outliers for VAKBN in 2017/3 Timestamp: 2017-03-06 09:15:00+00:00, Price: 5.4613 Timestamp: 2017-03-06 09:45:00+00:00, Price: 5.4613 Timestamp: 2017-03-06 10:00:00+00:00, Price: 5.4613 Timestamp: 2017-03-06 10:45:00+00:00, Price: 5.4613 Timestamp: 2017-03-06 11:00:00+00:00, Price: 5.4711 Timestamp: 2017-03-06 11:15:00+00:00, Price: 5.4809 Timestamp: 2017-03-06 11:30:00+00:00, Price: 5.4711 Timestamp: 2017-03-06 11:45:00+00:00, Price: 5.4613 Timestamp: 2017-03-06 12:00:00+00:00, Price: 5.4711 Timestamp: 2017-03-06 12:15:00+00:00, Price: 5.4906 Timestamp: 2017-03-06 12:30:00+00:00, Price: 5.5005 Timestamp: 2017-03-06 12:45:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 13:00:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 13:15:00+00:00, Price: 5.52 Timestamp: 2017-03-06 13:30:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 13:45:00+00:00, Price: 5.5102 Timestamp: 2017-03-06 14:00:00+00:00, Price: 5.4711 Timestamp: 2017-03-06 14:30:00+00:00, Price: 5.4613 Timestamp: 2017-03-06 14:45:00+00:00, Price: 5.4711 Timestamp: 2017-03-06 15:00:00+00:00, Price: 5.4906 Timestamp: 2017-03-07 06:45:00+00:00, Price: 5.4906 Timestamp: 2017-03-07 07:00:00+00:00, Price: 5.4809 Timestamp: 2017-03-07 07:15:00+00:00, Price: 5.5005 Timestamp: 2017-03-07 07:30:00+00:00, Price: 5.4711
NO OUTLIERS
Upper Outliers for TUPRS in 2017/3 Timestamp: 2017-03-21 08:15:00+00:00, Price: 67.4099 Timestamp: 2017-03-21 08:30:00+00:00, Price: 67.7422 Timestamp: 2017-03-21 08:45:00+00:00, Price: 67.5945 Timestamp: 2017-03-21 09:00:00+00:00, Price: 67.3731 Timestamp: 2017-03-21 09:15:00+00:00, Price: 67.3731 Timestamp: 2017-03-21 09:45:00+00:00, Price: 67.3362 Timestamp: 2017-03-21 10:45:00+00:00, Price: 67.3362 Timestamp: 2017-03-21 15:00:00+00:00, Price: 67.3362 Lower Outliers for TUPRS in 2017/3 Timestamp: 2017-03-01 06:45:00+00:00, Price: 63.0538 Timestamp: 2017-03-01 07:00:00+00:00, Price: 63.0907 Timestamp: 2017-03-01 07:15:00+00:00, Price: 62.98 Timestamp: 2017-03-01 07:30:00+00:00, Price: 63.1276 Timestamp: 2017-03-01 07:45:00+00:00, Price: 63.1276 Timestamp: 2017-03-01 08:00:00+00:00, Price: 63.1276 Timestamp: 2017-03-01 08:15:00+00:00, Price: 63.2384 Timestamp: 2017-03-01 08:30:00+00:00, Price: 63.3491 Timestamp: 2017-03-01 08:45:00+00:00, Price: 63.2753 Timestamp: 2017-03-01 09:00:00+00:00, Price: 63.4599 Timestamp: 2017-03-01 09:15:00+00:00, Price: 63.423 Timestamp: 2017-03-01 09:30:00+00:00, Price: 63.4599 Timestamp: 2017-03-01 09:45:00+00:00, Price: 63.4599 Timestamp: 2017-03-01 10:45:00+00:00, Price: 63.3491 Timestamp: 2017-03-01 11:00:00+00:00, Price: 63.4968 Timestamp: 2017-03-01 11:15:00+00:00, Price: 63.4599 Timestamp: 2017-03-01 11:30:00+00:00, Price: 63.423 Timestamp: 2017-03-01 11:45:00+00:00, Price: 63.4968 Timestamp: 2017-03-01 12:00:00+00:00, Price: 63.5706 Timestamp: 2017-03-01 12:15:00+00:00, Price: 63.6075 Timestamp: 2017-03-01 12:30:00+00:00, Price: 63.5706 Timestamp: 2017-03-01 12:45:00+00:00, Price: 63.4599 Timestamp: 2017-03-01 13:00:00+00:00, Price: 63.6445 Timestamp: 2017-03-01 13:15:00+00:00, Price: 63.9029 Timestamp: 2017-03-01 13:30:00+00:00, Price: 63.7552 Timestamp: 2017-03-01 14:00:00+00:00, Price: 63.6814 Timestamp: 2017-03-01 14:15:00+00:00, Price: 63.7183 Timestamp: 2017-03-01 14:30:00+00:00, Price: 63.9029 Timestamp: 2017-03-01 14:45:00+00:00, Price: 63.9029 Timestamp: 2017-03-01 15:00:00+00:00, Price: 63.866 Timestamp: 2017-03-02 06:45:00+00:00, Price: 63.866 Timestamp: 2017-03-02 08:15:00+00:00, Price: 63.8291 Timestamp: 2017-03-02 08:30:00+00:00, Price: 63.8291 Timestamp: 2017-03-02 08:45:00+00:00, Price: 63.423 Timestamp: 2017-03-02 09:00:00+00:00, Price: 63.3861 Timestamp: 2017-03-02 09:15:00+00:00, Price: 63.5338 Timestamp: 2017-03-02 09:30:00+00:00, Price: 63.4599 Timestamp: 2017-03-02 09:45:00+00:00, Price: 63.3861 Timestamp: 2017-03-02 10:45:00+00:00, Price: 63.3861 Timestamp: 2017-03-02 11:00:00+00:00, Price: 63.2753 Timestamp: 2017-03-02 11:15:00+00:00, Price: 63.1645 Timestamp: 2017-03-02 11:30:00+00:00, Price: 63.2015 Timestamp: 2017-03-02 11:45:00+00:00, Price: 63.1645 Timestamp: 2017-03-02 12:00:00+00:00, Price: 63.1276 Timestamp: 2017-03-02 12:15:00+00:00, Price: 63.2753 Timestamp: 2017-03-02 12:30:00+00:00, Price: 63.1645 Timestamp: 2017-03-02 12:45:00+00:00, Price: 63.0907 Timestamp: 2017-03-02 13:00:00+00:00, Price: 63.0538 Timestamp: 2017-03-02 13:15:00+00:00, Price: 63.0169 Timestamp: 2017-03-02 13:30:00+00:00, Price: 63.0538 Timestamp: 2017-03-02 13:45:00+00:00, Price: 63.0538 Timestamp: 2017-03-02 14:00:00+00:00, Price: 63.1645 Timestamp: 2017-03-02 14:15:00+00:00, Price: 63.5338 Timestamp: 2017-03-02 14:30:00+00:00, Price: 63.4968 Timestamp: 2017-03-02 14:45:00+00:00, Price: 63.5706 Timestamp: 2017-03-02 15:00:00+00:00, Price: 63.7921 Timestamp: 2017-03-03 06:45:00+00:00, Price: 63.6814 Timestamp: 2017-03-03 07:00:00+00:00, Price: 63.1276 Timestamp: 2017-03-03 07:15:00+00:00, Price: 63.4968 Timestamp: 2017-03-03 07:30:00+00:00, Price: 63.3122 Timestamp: 2017-03-03 07:45:00+00:00, Price: 63.2384 Timestamp: 2017-03-03 08:00:00+00:00, Price: 63.3861 Timestamp: 2017-03-03 08:15:00+00:00, Price: 63.6445
Upper Outliers for TCELL in 2017/3 Timestamp: 2017-03-06 07:00:00+00:00, Price: 10.4275 Timestamp: 2017-03-06 07:15:00+00:00, Price: 10.4275 Timestamp: 2017-03-06 07:30:00+00:00, Price: 10.4694 Timestamp: 2017-03-06 07:45:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 08:00:00+00:00, Price: 10.4778 Timestamp: 2017-03-06 08:15:00+00:00, Price: 10.4778 Timestamp: 2017-03-06 08:30:00+00:00, Price: 10.4443 Timestamp: 2017-03-06 08:45:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 09:00:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 09:15:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 09:30:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 09:45:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 10:00:00+00:00, Price: 10.45685 Timestamp: 2017-03-06 10:45:00+00:00, Price: 10.461 Timestamp: 2017-03-06 11:00:00+00:00, Price: 10.4778 Timestamp: 2017-03-06 11:15:00+00:00, Price: 10.4443 Timestamp: 2017-03-06 11:30:00+00:00, Price: 10.4944 Timestamp: 2017-03-06 11:45:00+00:00, Price: 10.4778 Timestamp: 2017-03-06 12:00:00+00:00, Price: 10.4694 Timestamp: 2017-03-06 12:15:00+00:00, Price: 10.461 Timestamp: 2017-03-06 12:30:00+00:00, Price: 10.461 Timestamp: 2017-03-06 12:45:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 13:00:00+00:00, Price: 10.461 Timestamp: 2017-03-06 13:15:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 13:30:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 13:45:00+00:00, Price: 10.4527 Timestamp: 2017-03-06 14:00:00+00:00, Price: 10.436 Timestamp: 2017-03-06 14:15:00+00:00, Price: 10.4275 Timestamp: 2017-03-06 14:30:00+00:00, Price: 10.4109 Timestamp: 2017-03-06 14:45:00+00:00, Price: 10.3858 Timestamp: 2017-03-06 15:00:00+00:00, Price: 10.4109 Timestamp: 2017-03-07 06:45:00+00:00, Price: 10.4443 Timestamp: 2017-03-07 07:00:00+00:00, Price: 10.4109 Timestamp: 2017-03-07 07:15:00+00:00, Price: 10.4109 Timestamp: 2017-03-07 07:30:00+00:00, Price: 10.3858 Timestamp: 2017-03-20 08:00:00+00:00, Price: 10.3775 Timestamp: 2017-03-20 08:15:00+00:00, Price: 10.3775 Timestamp: 2017-03-20 13:00:00+00:00, Price: 10.3942 Timestamp: 2017-03-20 13:15:00+00:00, Price: 10.4193 Timestamp: 2017-03-20 13:30:00+00:00, Price: 10.4109 Timestamp: 2017-03-20 13:45:00+00:00, Price: 10.3942 Timestamp: 2017-03-20 14:00:00+00:00, Price: 10.4024 Timestamp: 2017-03-20 14:15:00+00:00, Price: 10.4024 Timestamp: 2017-03-20 14:30:00+00:00, Price: 10.3858 Timestamp: 2017-03-20 14:45:00+00:00, Price: 10.3775 Timestamp: 2017-03-20 15:00:00+00:00, Price: 10.3858 Timestamp: 2017-03-21 06:45:00+00:00, Price: 10.4275 Timestamp: 2017-03-21 07:00:00+00:00, Price: 10.4275 Timestamp: 2017-03-21 07:15:00+00:00, Price: 10.3858 Timestamp: 2017-03-21 07:30:00+00:00, Price: 10.4109 Timestamp: 2017-03-21 07:45:00+00:00, Price: 10.3775 Timestamp: 2017-03-21 08:00:00+00:00, Price: 10.4275 Timestamp: 2017-03-21 08:15:00+00:00, Price: 10.4193 Timestamp: 2017-03-21 08:30:00+00:00, Price: 10.4024 Timestamp: 2017-03-21 08:45:00+00:00, Price: 10.3942 Timestamp: 2017-03-21 09:00:00+00:00, Price: 10.3775 Timestamp: 2017-03-21 09:15:00+00:00, Price: 10.3775 Lower Outliers for TCELL in 2017/3 Timestamp: 2017-03-13 07:30:00+00:00, Price: 9.9092 Timestamp: 2017-03-13 07:45:00+00:00, Price: 9.9175 Timestamp: 2017-03-13 08:00:00+00:00, Price: 9.9259 Timestamp: 2017-03-13 09:00:00+00:00, Price: 9.9342 Timestamp: 2017-03-13 09:15:00+00:00, Price: 9.9342 Timestamp: 2017-03-13 09:30:00+00:00, Price: 9.9342 Timestamp: 2017-03-13 09:45:00+00:00, Price: 9.9092 Timestamp: 2017-03-13 10:45:00+00:00, Price: 9.9175 Timestamp: 2017-03-13 11:00:00+00:00, Price: 9.9342 Timestamp: 2017-03-31 07:45:00+00:00, Price: 9.9342
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for TUPRS in 2017/4 Timestamp: 2017-04-26 11:45:00+00:00, Price: 69.3235 Timestamp: 2017-04-26 12:45:00+00:00, Price: 69.3235 Timestamp: 2017-04-26 13:15:00+00:00, Price: 69.3632 Timestamp: 2017-04-26 13:45:00+00:00, Price: 69.3235 Timestamp: 2017-04-26 14:00:00+00:00, Price: 69.6409 Timestamp: 2017-04-26 14:15:00+00:00, Price: 69.6013 Timestamp: 2017-04-26 14:30:00+00:00, Price: 69.5616 Timestamp: 2017-04-26 14:45:00+00:00, Price: 69.7203 Timestamp: 2017-04-26 15:00:00+00:00, Price: 69.8393 Timestamp: 2017-04-27 06:45:00+00:00, Price: 69.7203 Timestamp: 2017-04-27 07:00:00+00:00, Price: 70.1964 Timestamp: 2017-04-27 07:15:00+00:00, Price: 70.0774 Timestamp: 2017-04-27 07:30:00+00:00, Price: 70.1171 Timestamp: 2017-04-27 07:45:00+00:00, Price: 70.1568 Timestamp: 2017-04-27 08:00:00+00:00, Price: 70.1171 Timestamp: 2017-04-27 08:15:00+00:00, Price: 70.1568 Timestamp: 2017-04-27 08:30:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 08:45:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 09:00:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 09:15:00+00:00, Price: 70.0774 Timestamp: 2017-04-27 09:30:00+00:00, Price: 70.0774 Timestamp: 2017-04-27 09:45:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 10:00:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 10:45:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 11:00:00+00:00, Price: 70.0377 Timestamp: 2017-04-27 11:15:00+00:00, Price: 70.1568 Timestamp: 2017-04-27 11:30:00+00:00, Price: 70.4742 Timestamp: 2017-04-27 11:45:00+00:00, Price: 70.3949 Timestamp: 2017-04-27 12:00:00+00:00, Price: 70.2758 Timestamp: 2017-04-27 12:15:00+00:00, Price: 70.3155 Timestamp: 2017-04-27 12:30:00+00:00, Price: 70.4345 Timestamp: 2017-04-27 12:45:00+00:00, Price: 70.4742 Timestamp: 2017-04-27 13:00:00+00:00, Price: 70.3552 Timestamp: 2017-04-27 13:15:00+00:00, Price: 70.3552 Timestamp: 2017-04-27 13:30:00+00:00, Price: 70.3155 Timestamp: 2017-04-27 13:45:00+00:00, Price: 70.4345 Timestamp: 2017-04-27 14:00:00+00:00, Price: 70.5536 Timestamp: 2017-04-27 14:15:00+00:00, Price: 70.633 Timestamp: 2017-04-27 14:30:00+00:00, Price: 70.4345 Timestamp: 2017-04-27 14:45:00+00:00, Price: 70.3949 Timestamp: 2017-04-27 15:00:00+00:00, Price: 70.3949 Timestamp: 2017-04-28 06:45:00+00:00, Price: 70.1964 Timestamp: 2017-04-28 07:00:00+00:00, Price: 70.2758 Timestamp: 2017-04-28 07:15:00+00:00, Price: 70.2758 Timestamp: 2017-04-28 07:30:00+00:00, Price: 70.1171 Timestamp: 2017-04-28 07:45:00+00:00, Price: 70.0377 Timestamp: 2017-04-28 08:00:00+00:00, Price: 70.0377 Timestamp: 2017-04-28 08:15:00+00:00, Price: 70.0774 Timestamp: 2017-04-28 08:30:00+00:00, Price: 70.0377 Timestamp: 2017-04-28 08:45:00+00:00, Price: 70.0377 Timestamp: 2017-04-28 09:00:00+00:00, Price: 69.9187 Timestamp: 2017-04-28 09:15:00+00:00, Price: 69.998 Timestamp: 2017-04-28 09:30:00+00:00, Price: 69.998 Timestamp: 2017-04-28 09:45:00+00:00, Price: 70.0774 Timestamp: 2017-04-28 10:00:00+00:00, Price: 70.0774 Timestamp: 2017-04-28 10:45:00+00:00, Price: 70.0774 Timestamp: 2017-04-28 11:00:00+00:00, Price: 70.1171 Timestamp: 2017-04-28 11:15:00+00:00, Price: 70.1171 Timestamp: 2017-04-28 11:30:00+00:00, Price: 70.1964 Timestamp: 2017-04-28 11:45:00+00:00, Price: 70.3155 Timestamp: 2017-04-28 12:00:00+00:00, Price: 70.2758 Timestamp: 2017-04-28 12:15:00+00:00, Price: 70.3155 Timestamp: 2017-04-28 12:30:00+00:00, Price: 70.2361 Timestamp: 2017-04-28 12:45:00+00:00, Price: 70.4742 Timestamp: 2017-04-28 13:00:00+00:00, Price: 70.4742 Timestamp: 2017-04-28 13:15:00+00:00, Price: 70.4345 Timestamp: 2017-04-28 13:30:00+00:00, Price: 70.4345 Timestamp: 2017-04-28 13:45:00+00:00, Price: 70.4742 Timestamp: 2017-04-28 14:00:00+00:00, Price: 70.4345 Timestamp: 2017-04-28 14:15:00+00:00, Price: 70.4345 Timestamp: 2017-04-28 14:30:00+00:00, Price: 71.0298 Timestamp: 2017-04-28 14:45:00+00:00, Price: 71.0298 Timestamp: 2017-04-28 15:00:00+00:00, Price: 71.0298 Lower Outliers for TUPRS in 2017/4 Timestamp: 2017-04-03 08:00:00+00:00, Price: 65.7488 Timestamp: 2017-04-03 08:45:00+00:00, Price: 65.6011 Timestamp: 2017-04-03 09:00:00+00:00, Price: 65.7856 Timestamp: 2017-04-03 11:15:00+00:00, Price: 65.7856 Timestamp: 2017-04-03 11:30:00+00:00, Price: 65.7488 Timestamp: 2017-04-03 12:00:00+00:00, Price: 65.6379 Timestamp: 2017-04-03 12:15:00+00:00, Price: 65.4535 Timestamp: 2017-04-03 12:30:00+00:00, Price: 65.5641 Timestamp: 2017-04-03 12:45:00+00:00, Price: 65.6011 Timestamp: 2017-04-03 13:00:00+00:00, Price: 65.5272 Timestamp: 2017-04-03 13:15:00+00:00, Price: 65.4903 Timestamp: 2017-04-03 13:30:00+00:00, Price: 65.5641 Timestamp: 2017-04-03 13:45:00+00:00, Price: 65.5272 Timestamp: 2017-04-03 14:00:00+00:00, Price: 65.6011 Timestamp: 2017-04-03 14:15:00+00:00, Price: 65.7488 Timestamp: 2017-04-04 15:00:00+00:00, Price: 65.7125 Timestamp: 2017-04-05 06:45:00+00:00, Price: 65.7125
NO OUTLIERS
Upper Outliers for THYAO in 2017/4 Timestamp: 2017-04-26 07:45:00+00:00, Price: 6.09 Timestamp: 2017-04-26 08:00:00+00:00, Price: 6.14 Timestamp: 2017-04-26 08:15:00+00:00, Price: 6.13 Timestamp: 2017-04-26 08:30:00+00:00, Price: 6.11 Timestamp: 2017-04-26 08:45:00+00:00, Price: 6.1 Timestamp: 2017-04-26 09:00:00+00:00, Price: 6.11 Timestamp: 2017-04-26 09:15:00+00:00, Price: 6.1 Timestamp: 2017-04-26 09:30:00+00:00, Price: 6.1 Timestamp: 2017-04-26 09:45:00+00:00, Price: 6.1 Timestamp: 2017-04-26 10:00:00+00:00, Price: 6.1 Timestamp: 2017-04-26 10:45:00+00:00, Price: 6.1 Timestamp: 2017-04-26 11:00:00+00:00, Price: 6.1 Timestamp: 2017-04-26 11:15:00+00:00, Price: 6.11 Timestamp: 2017-04-26 11:30:00+00:00, Price: 6.14 Timestamp: 2017-04-26 11:45:00+00:00, Price: 6.13 Timestamp: 2017-04-26 12:00:00+00:00, Price: 6.11 Timestamp: 2017-04-26 12:15:00+00:00, Price: 6.12 Timestamp: 2017-04-26 12:30:00+00:00, Price: 6.12 Timestamp: 2017-04-26 12:45:00+00:00, Price: 6.12 Timestamp: 2017-04-26 13:00:00+00:00, Price: 6.15 Timestamp: 2017-04-26 13:15:00+00:00, Price: 6.14 Timestamp: 2017-04-26 13:30:00+00:00, Price: 6.12 Timestamp: 2017-04-26 13:45:00+00:00, Price: 6.12 Timestamp: 2017-04-26 14:00:00+00:00, Price: 6.1 Timestamp: 2017-04-26 14:15:00+00:00, Price: 6.09 Timestamp: 2017-04-26 14:30:00+00:00, Price: 6.12 Timestamp: 2017-04-26 14:45:00+00:00, Price: 6.1 Timestamp: 2017-04-26 15:00:00+00:00, Price: 6.1 Timestamp: 2017-04-27 06:45:00+00:00, Price: 6.12 Timestamp: 2017-04-27 07:00:00+00:00, Price: 6.15 Timestamp: 2017-04-27 07:15:00+00:00, Price: 6.13 Timestamp: 2017-04-27 07:30:00+00:00, Price: 6.19 Timestamp: 2017-04-27 07:45:00+00:00, Price: 6.2 Timestamp: 2017-04-27 08:00:00+00:00, Price: 6.19 Timestamp: 2017-04-27 08:15:00+00:00, Price: 6.19 Timestamp: 2017-04-27 08:30:00+00:00, Price: 6.18 Timestamp: 2017-04-27 08:45:00+00:00, Price: 6.17 Timestamp: 2017-04-27 09:00:00+00:00, Price: 6.18 Timestamp: 2017-04-27 09:15:00+00:00, Price: 6.17 Timestamp: 2017-04-27 09:30:00+00:00, Price: 6.16 Timestamp: 2017-04-27 09:45:00+00:00, Price: 6.17 Timestamp: 2017-04-27 10:00:00+00:00, Price: 6.17 Timestamp: 2017-04-27 10:45:00+00:00, Price: 6.17 Timestamp: 2017-04-27 11:00:00+00:00, Price: 6.16 Timestamp: 2017-04-27 11:15:00+00:00, Price: 6.18 Timestamp: 2017-04-27 11:30:00+00:00, Price: 6.17 Timestamp: 2017-04-27 11:45:00+00:00, Price: 6.17 Timestamp: 2017-04-27 12:00:00+00:00, Price: 6.17 Timestamp: 2017-04-27 12:15:00+00:00, Price: 6.18 Timestamp: 2017-04-27 12:30:00+00:00, Price: 6.17 Timestamp: 2017-04-27 12:45:00+00:00, Price: 6.17 Timestamp: 2017-04-27 13:00:00+00:00, Price: 6.2 Timestamp: 2017-04-27 13:15:00+00:00, Price: 6.17 Timestamp: 2017-04-27 13:30:00+00:00, Price: 6.16 Timestamp: 2017-04-27 13:45:00+00:00, Price: 6.17 Timestamp: 2017-04-27 14:00:00+00:00, Price: 6.13 Timestamp: 2017-04-27 14:15:00+00:00, Price: 6.12 Timestamp: 2017-04-27 14:30:00+00:00, Price: 6.12 Timestamp: 2017-04-27 14:45:00+00:00, Price: 6.06 Timestamp: 2017-04-27 15:00:00+00:00, Price: 6.06 Timestamp: 2017-04-28 06:45:00+00:00, Price: 6.08 Timestamp: 2017-04-28 07:00:00+00:00, Price: 6.13 Timestamp: 2017-04-28 07:15:00+00:00, Price: 6.13 Timestamp: 2017-04-28 07:30:00+00:00, Price: 6.1 Timestamp: 2017-04-28 07:45:00+00:00, Price: 6.1 Timestamp: 2017-04-28 08:00:00+00:00, Price: 6.09 Timestamp: 2017-04-28 08:15:00+00:00, Price: 6.08 Timestamp: 2017-04-28 08:45:00+00:00, Price: 6.05 Timestamp: 2017-04-28 09:30:00+00:00, Price: 6.05 Timestamp: 2017-04-28 09:45:00+00:00, Price: 6.05 Timestamp: 2017-04-28 10:00:00+00:00, Price: 6.05 Timestamp: 2017-04-28 10:45:00+00:00, Price: 6.05 Timestamp: 2017-04-28 11:00:00+00:00, Price: 6.06 Timestamp: 2017-04-28 11:15:00+00:00, Price: 6.05 Timestamp: 2017-04-28 11:30:00+00:00, Price: 6.06 Timestamp: 2017-04-28 11:45:00+00:00, Price: 6.07 Timestamp: 2017-04-28 12:00:00+00:00, Price: 6.05 Timestamp: 2017-04-28 12:15:00+00:00, Price: 6.07 Timestamp: 2017-04-28 12:30:00+00:00, Price: 6.06 Timestamp: 2017-04-28 12:45:00+00:00, Price: 6.07 Timestamp: 2017-04-28 13:00:00+00:00, Price: 6.08 Timestamp: 2017-04-28 13:15:00+00:00, Price: 6.08 Timestamp: 2017-04-28 13:30:00+00:00, Price: 6.08 Timestamp: 2017-04-28 13:45:00+00:00, Price: 6.06 Timestamp: 2017-04-28 14:00:00+00:00, Price: 6.05 Timestamp: 2017-04-28 14:30:00+00:00, Price: 6.06 Timestamp: 2017-04-28 14:45:00+00:00, Price: 6.06 Timestamp: 2017-04-28 15:00:00+00:00, Price: 6.06
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for TUPRS in 2017/5 Timestamp: 2017-05-04 15:00:00+00:00, Price: 69.2838
Upper Outliers for TCELL in 2017/5 Timestamp: 2017-05-03 11:15:00+00:00, Price: 10.4527 Timestamp: 2017-05-03 11:45:00+00:00, Price: 10.4527 Timestamp: 2017-05-03 12:15:00+00:00, Price: 10.4443 Timestamp: 2017-05-03 12:30:00+00:00, Price: 10.4443
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for ARCLK in 2017/6 Timestamp: 2017-06-09 09:15:00+00:00, Price: 25.7522 Timestamp: 2017-06-09 09:30:00+00:00, Price: 25.7328 Timestamp: 2017-06-09 09:45:00+00:00, Price: 25.7328 Timestamp: 2017-06-09 10:45:00+00:00, Price: 25.7135 Timestamp: 2017-06-09 11:00:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 11:15:00+00:00, Price: 25.7522 Timestamp: 2017-06-09 11:30:00+00:00, Price: 25.7328 Timestamp: 2017-06-09 11:45:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 12:00:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 12:15:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 12:30:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 12:45:00+00:00, Price: 25.7908 Timestamp: 2017-06-09 13:00:00+00:00, Price: 25.7328 Timestamp: 2017-06-09 13:15:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 13:30:00+00:00, Price: 25.7715 Timestamp: 2017-06-09 13:45:00+00:00, Price: 25.7908 Timestamp: 2017-06-09 14:00:00+00:00, Price: 25.8101 Timestamp: 2017-06-09 14:15:00+00:00, Price: 25.8294 Timestamp: 2017-06-12 07:30:00+00:00, Price: 25.7522 Lower Outliers for ARCLK in 2017/6 Timestamp: 2017-06-01 06:45:00+00:00, Price: 23.7059 Timestamp: 2017-06-01 07:00:00+00:00, Price: 23.7831 Timestamp: 2017-06-01 07:15:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 07:30:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 07:45:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 08:00:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 08:15:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 08:30:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 08:45:00+00:00, Price: 23.6673 Timestamp: 2017-06-01 09:00:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 09:15:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 09:30:00+00:00, Price: 23.7638 Timestamp: 2017-06-01 09:45:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 10:45:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 11:00:00+00:00, Price: 23.7638 Timestamp: 2017-06-01 11:15:00+00:00, Price: 23.6866 Timestamp: 2017-06-01 11:30:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 11:45:00+00:00, Price: 23.7638 Timestamp: 2017-06-01 12:00:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 12:15:00+00:00, Price: 23.8024 Timestamp: 2017-06-01 12:30:00+00:00, Price: 23.7831 Timestamp: 2017-06-01 12:45:00+00:00, Price: 23.7831 Timestamp: 2017-06-01 13:00:00+00:00, Price: 23.7445 Timestamp: 2017-06-01 13:15:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 13:30:00+00:00, Price: 23.6673 Timestamp: 2017-06-01 13:45:00+00:00, Price: 23.7059 Timestamp: 2017-06-01 14:00:00+00:00, Price: 23.648 Timestamp: 2017-06-01 14:15:00+00:00, Price: 23.6673 Timestamp: 2017-06-01 14:30:00+00:00, Price: 23.7252 Timestamp: 2017-06-01 14:45:00+00:00, Price: 23.841 Timestamp: 2017-06-01 15:00:00+00:00, Price: 23.9182 Timestamp: 2017-06-02 06:45:00+00:00, Price: 24.0147 Timestamp: 2017-06-02 07:00:00+00:00, Price: 24.4394 Timestamp: 2017-06-02 07:15:00+00:00, Price: 24.3236 Timestamp: 2017-06-02 07:30:00+00:00, Price: 24.3815 Timestamp: 2017-06-02 07:45:00+00:00, Price: 24.4201 Timestamp: 2017-06-02 08:00:00+00:00, Price: 24.3815 Timestamp: 2017-06-02 08:15:00+00:00, Price: 24.3622 Timestamp: 2017-06-02 08:30:00+00:00, Price: 24.3429 Timestamp: 2017-06-02 08:45:00+00:00, Price: 24.4394 Timestamp: 2017-06-02 09:00:00+00:00, Price: 24.4588
Lower Outliers for TUPRS in 2017/6 Timestamp: 2017-06-01 06:45:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 07:00:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 07:15:00+00:00, Price: 75.6328 Timestamp: 2017-06-01 07:30:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 07:45:00+00:00, Price: 75.7519 Timestamp: 2017-06-01 08:00:00+00:00, Price: 75.7122 Timestamp: 2017-06-01 08:15:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 08:30:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 08:45:00+00:00, Price: 75.8312 Timestamp: 2017-06-01 09:00:00+00:00, Price: 75.8312 Timestamp: 2017-06-01 09:15:00+00:00, Price: 75.8312 Timestamp: 2017-06-01 09:30:00+00:00, Price: 75.8312 Timestamp: 2017-06-01 09:45:00+00:00, Price: 75.9106 Timestamp: 2017-06-01 10:45:00+00:00, Price: 75.7122 Timestamp: 2017-06-01 11:00:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 11:15:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 11:30:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 11:45:00+00:00, Price: 75.8709 Timestamp: 2017-06-01 12:00:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 12:15:00+00:00, Price: 76.0693 Timestamp: 2017-06-01 12:30:00+00:00, Price: 75.9503 Timestamp: 2017-06-01 12:45:00+00:00, Price: 76.1884 Timestamp: 2017-06-01 13:00:00+00:00, Price: 76.0693 Timestamp: 2017-06-01 13:15:00+00:00, Price: 76.1884 Timestamp: 2017-06-01 13:30:00+00:00, Price: 75.9503 Timestamp: 2017-06-01 13:45:00+00:00, Price: 76.109 Timestamp: 2017-06-01 14:00:00+00:00, Price: 75.9503 Timestamp: 2017-06-01 14:15:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 14:30:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 14:45:00+00:00, Price: 75.7915 Timestamp: 2017-06-01 15:00:00+00:00, Price: 75.9503 Timestamp: 2017-06-02 06:45:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 07:00:00+00:00, Price: 76.5059 Timestamp: 2017-06-02 07:15:00+00:00, Price: 76.3868 Timestamp: 2017-06-02 07:30:00+00:00, Price: 76.5059 Timestamp: 2017-06-02 08:15:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 08:30:00+00:00, Price: 76.4662 Timestamp: 2017-06-02 08:45:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 09:00:00+00:00, Price: 76.4662 Timestamp: 2017-06-02 09:15:00+00:00, Price: 76.4662 Timestamp: 2017-06-02 09:30:00+00:00, Price: 76.3868 Timestamp: 2017-06-02 09:45:00+00:00, Price: 76.3868 Timestamp: 2017-06-02 10:00:00+00:00, Price: 76.40665 Timestamp: 2017-06-02 10:45:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 11:00:00+00:00, Price: 76.5059 Timestamp: 2017-06-02 11:15:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 11:30:00+00:00, Price: 76.4662 Timestamp: 2017-06-02 11:45:00+00:00, Price: 76.4662 Timestamp: 2017-06-02 12:00:00+00:00, Price: 76.5455 Timestamp: 2017-06-02 12:15:00+00:00, Price: 76.5059 Timestamp: 2017-06-02 12:30:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 12:45:00+00:00, Price: 76.3471 Timestamp: 2017-06-02 13:00:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 13:15:00+00:00, Price: 76.3868 Timestamp: 2017-06-02 13:30:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 13:45:00+00:00, Price: 76.4662 Timestamp: 2017-06-02 14:00:00+00:00, Price: 76.4265 Timestamp: 2017-06-02 14:15:00+00:00, Price: 76.5059 Timestamp: 2017-06-02 14:45:00+00:00, Price: 76.3074
NO OUTLIERS
Upper Outliers for THYAO in 2017/6 Timestamp: 2017-06-23 08:00:00+00:00, Price: 7.76 Timestamp: 2017-06-23 08:45:00+00:00, Price: 7.77 Timestamp: 2017-06-23 15:00:00+00:00, Price: 7.77 Timestamp: 2017-06-28 07:00:00+00:00, Price: 7.83 Timestamp: 2017-06-28 07:15:00+00:00, Price: 7.9 Timestamp: 2017-06-28 07:30:00+00:00, Price: 7.93 Timestamp: 2017-06-28 07:45:00+00:00, Price: 7.92 Timestamp: 2017-06-28 08:00:00+00:00, Price: 7.87 Timestamp: 2017-06-28 08:15:00+00:00, Price: 7.88 Timestamp: 2017-06-28 08:30:00+00:00, Price: 7.88 Timestamp: 2017-06-28 08:45:00+00:00, Price: 7.86 Timestamp: 2017-06-28 09:00:00+00:00, Price: 7.88 Timestamp: 2017-06-28 09:15:00+00:00, Price: 7.88 Timestamp: 2017-06-28 09:30:00+00:00, Price: 7.88 Timestamp: 2017-06-28 09:45:00+00:00, Price: 7.89 Timestamp: 2017-06-28 10:00:00+00:00, Price: 7.89 Timestamp: 2017-06-28 10:45:00+00:00, Price: 7.89 Timestamp: 2017-06-28 11:00:00+00:00, Price: 7.89 Timestamp: 2017-06-28 11:15:00+00:00, Price: 7.9 Timestamp: 2017-06-28 11:30:00+00:00, Price: 7.91 Timestamp: 2017-06-28 11:45:00+00:00, Price: 7.93 Timestamp: 2017-06-28 12:00:00+00:00, Price: 7.92 Timestamp: 2017-06-28 12:15:00+00:00, Price: 7.93 Timestamp: 2017-06-28 12:30:00+00:00, Price: 7.95 Timestamp: 2017-06-28 12:45:00+00:00, Price: 7.94 Timestamp: 2017-06-28 13:00:00+00:00, Price: 7.96 Timestamp: 2017-06-28 13:15:00+00:00, Price: 7.95 Timestamp: 2017-06-28 13:30:00+00:00, Price: 7.94 Timestamp: 2017-06-28 13:45:00+00:00, Price: 7.94 Timestamp: 2017-06-28 14:00:00+00:00, Price: 7.95 Timestamp: 2017-06-28 14:15:00+00:00, Price: 7.99 Timestamp: 2017-06-28 14:30:00+00:00, Price: 8.01 Timestamp: 2017-06-28 14:45:00+00:00, Price: 7.99 Timestamp: 2017-06-28 15:00:00+00:00, Price: 7.99 Timestamp: 2017-06-29 06:45:00+00:00, Price: 8.02 Timestamp: 2017-06-29 07:00:00+00:00, Price: 8.09 Timestamp: 2017-06-29 07:15:00+00:00, Price: 8.09 Timestamp: 2017-06-29 07:30:00+00:00, Price: 8.08 Timestamp: 2017-06-29 07:45:00+00:00, Price: 8.05 Timestamp: 2017-06-29 08:00:00+00:00, Price: 8.06 Timestamp: 2017-06-29 08:15:00+00:00, Price: 8.06 Timestamp: 2017-06-29 08:30:00+00:00, Price: 8.04 Timestamp: 2017-06-29 08:45:00+00:00, Price: 8.03 Timestamp: 2017-06-29 09:00:00+00:00, Price: 8.03 Timestamp: 2017-06-29 09:15:00+00:00, Price: 8.01 Timestamp: 2017-06-29 09:30:00+00:00, Price: 7.97 Timestamp: 2017-06-29 09:45:00+00:00, Price: 7.99 Timestamp: 2017-06-29 10:45:00+00:00, Price: 7.99 Timestamp: 2017-06-29 11:00:00+00:00, Price: 7.98 Timestamp: 2017-06-29 11:15:00+00:00, Price: 7.95 Timestamp: 2017-06-29 11:30:00+00:00, Price: 7.95 Timestamp: 2017-06-29 11:45:00+00:00, Price: 7.89 Timestamp: 2017-06-29 12:00:00+00:00, Price: 7.88 Timestamp: 2017-06-29 12:15:00+00:00, Price: 7.88 Timestamp: 2017-06-29 12:30:00+00:00, Price: 7.84 Timestamp: 2017-06-29 12:45:00+00:00, Price: 7.86 Timestamp: 2017-06-29 13:00:00+00:00, Price: 7.86 Timestamp: 2017-06-29 13:15:00+00:00, Price: 7.83 Timestamp: 2017-06-29 13:30:00+00:00, Price: 7.82 Timestamp: 2017-06-29 13:45:00+00:00, Price: 7.79 Timestamp: 2017-06-29 14:00:00+00:00, Price: 7.8 Timestamp: 2017-06-29 14:15:00+00:00, Price: 7.82 Timestamp: 2017-06-29 14:30:00+00:00, Price: 7.83 Timestamp: 2017-06-29 14:45:00+00:00, Price: 7.83 Timestamp: 2017-06-29 15:00:00+00:00, Price: 7.81 Timestamp: 2017-06-30 06:45:00+00:00, Price: 7.84 Timestamp: 2017-06-30 07:00:00+00:00, Price: 7.88 Timestamp: 2017-06-30 07:15:00+00:00, Price: 7.88 Timestamp: 2017-06-30 07:30:00+00:00, Price: 7.92 Timestamp: 2017-06-30 07:45:00+00:00, Price: 7.94 Timestamp: 2017-06-30 08:00:00+00:00, Price: 7.97 Timestamp: 2017-06-30 08:15:00+00:00, Price: 7.96 Timestamp: 2017-06-30 08:30:00+00:00, Price: 7.91 Timestamp: 2017-06-30 08:45:00+00:00, Price: 7.93 Timestamp: 2017-06-30 09:00:00+00:00, Price: 7.94 Timestamp: 2017-06-30 09:15:00+00:00, Price: 7.93 Timestamp: 2017-06-30 09:30:00+00:00, Price: 7.93 Timestamp: 2017-06-30 09:45:00+00:00, Price: 7.94 Timestamp: 2017-06-30 10:45:00+00:00, Price: 7.94 Timestamp: 2017-06-30 11:00:00+00:00, Price: 7.97 Timestamp: 2017-06-30 11:15:00+00:00, Price: 7.96 Timestamp: 2017-06-30 11:30:00+00:00, Price: 7.96 Timestamp: 2017-06-30 11:45:00+00:00, Price: 7.97 Timestamp: 2017-06-30 12:00:00+00:00, Price: 7.94 Timestamp: 2017-06-30 12:15:00+00:00, Price: 7.95 Timestamp: 2017-06-30 12:30:00+00:00, Price: 7.92 Timestamp: 2017-06-30 12:45:00+00:00, Price: 7.92 Timestamp: 2017-06-30 13:00:00+00:00, Price: 7.94 Timestamp: 2017-06-30 13:15:00+00:00, Price: 7.93 Timestamp: 2017-06-30 13:30:00+00:00, Price: 7.93 Timestamp: 2017-06-30 13:45:00+00:00, Price: 7.94 Timestamp: 2017-06-30 14:00:00+00:00, Price: 7.98 Timestamp: 2017-06-30 14:15:00+00:00, Price: 7.99 Timestamp: 2017-06-30 14:30:00+00:00, Price: 8.0 Timestamp: 2017-06-30 14:45:00+00:00, Price: 8.05 Timestamp: 2017-06-30 15:00:00+00:00, Price: 8.05
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for TCELL in 2017/7 Timestamp: 2017-07-28 12:15:00+00:00, Price: 11.4711 Timestamp: 2017-07-28 12:30:00+00:00, Price: 11.4711 Timestamp: 2017-07-28 12:45:00+00:00, Price: 11.4711
Lower Outliers for THYAO in 2017/7 Timestamp: 2017-07-03 06:45:00+00:00, Price: 8.1 Timestamp: 2017-07-03 07:00:00+00:00, Price: 8.09 Timestamp: 2017-07-03 07:15:00+00:00, Price: 8.08 Timestamp: 2017-07-03 07:30:00+00:00, Price: 8.09 Timestamp: 2017-07-03 07:45:00+00:00, Price: 8.1 Timestamp: 2017-07-03 08:00:00+00:00, Price: 8.11 Timestamp: 2017-07-03 08:15:00+00:00, Price: 8.12 Timestamp: 2017-07-03 08:30:00+00:00, Price: 8.11 Timestamp: 2017-07-03 08:45:00+00:00, Price: 8.16 Timestamp: 2017-07-03 09:00:00+00:00, Price: 8.13 Timestamp: 2017-07-03 09:15:00+00:00, Price: 8.11 Timestamp: 2017-07-03 09:30:00+00:00, Price: 8.1 Timestamp: 2017-07-03 09:45:00+00:00, Price: 8.1 Timestamp: 2017-07-03 10:45:00+00:00, Price: 8.1 Timestamp: 2017-07-03 11:00:00+00:00, Price: 8.12 Timestamp: 2017-07-03 11:15:00+00:00, Price: 8.1 Timestamp: 2017-07-03 11:30:00+00:00, Price: 8.1 Timestamp: 2017-07-03 11:45:00+00:00, Price: 8.1 Timestamp: 2017-07-03 12:00:00+00:00, Price: 8.09 Timestamp: 2017-07-03 12:15:00+00:00, Price: 8.09 Timestamp: 2017-07-03 12:30:00+00:00, Price: 8.09 Timestamp: 2017-07-03 12:45:00+00:00, Price: 8.1 Timestamp: 2017-07-03 13:00:00+00:00, Price: 8.1 Timestamp: 2017-07-03 13:15:00+00:00, Price: 8.07 Timestamp: 2017-07-03 13:30:00+00:00, Price: 8.08 Timestamp: 2017-07-03 13:45:00+00:00, Price: 8.09 Timestamp: 2017-07-03 14:00:00+00:00, Price: 8.09 Timestamp: 2017-07-03 14:15:00+00:00, Price: 8.08 Timestamp: 2017-07-03 14:30:00+00:00, Price: 8.07 Timestamp: 2017-07-03 14:45:00+00:00, Price: 8.07 Timestamp: 2017-07-03 15:00:00+00:00, Price: 8.06 Timestamp: 2017-07-04 06:45:00+00:00, Price: 8.17 Timestamp: 2017-07-04 07:00:00+00:00, Price: 8.19 Timestamp: 2017-07-04 08:00:00+00:00, Price: 8.22 Timestamp: 2017-07-04 08:45:00+00:00, Price: 8.21 Timestamp: 2017-07-04 09:00:00+00:00, Price: 8.22 Timestamp: 2017-07-06 12:00:00+00:00, Price: 8.21 Timestamp: 2017-07-06 12:15:00+00:00, Price: 8.21 Timestamp: 2017-07-06 12:30:00+00:00, Price: 8.22 Timestamp: 2017-07-06 12:45:00+00:00, Price: 8.22 Timestamp: 2017-07-06 13:00:00+00:00, Price: 8.22
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for TUPRS in 2017/8 Timestamp: 2017-08-01 06:45:00+00:00, Price: 86.0293 Timestamp: 2017-08-01 07:00:00+00:00, Price: 86.4262 Timestamp: 2017-08-01 07:15:00+00:00, Price: 86.1881 Timestamp: 2017-08-01 07:30:00+00:00, Price: 86.0293 Timestamp: 2017-08-01 07:45:00+00:00, Price: 86.0293 Timestamp: 2017-08-01 08:00:00+00:00, Price: 86.0293 Timestamp: 2017-08-01 08:15:00+00:00, Price: 85.8706 Timestamp: 2017-08-01 08:30:00+00:00, Price: 85.7912 Timestamp: 2017-08-01 08:45:00+00:00, Price: 85.6325 Timestamp: 2017-08-01 09:00:00+00:00, Price: 84.2834 Timestamp: 2017-08-01 09:15:00+00:00, Price: 84.7596 Timestamp: 2017-08-01 09:30:00+00:00, Price: 84.7596 Timestamp: 2017-08-01 09:45:00+00:00, Price: 84.6008 Timestamp: 2017-08-01 10:45:00+00:00, Price: 84.6008 Timestamp: 2017-08-01 11:00:00+00:00, Price: 84.5215 Timestamp: 2017-08-01 11:15:00+00:00, Price: 84.9977 Timestamp: 2017-08-01 11:30:00+00:00, Price: 84.9183 Timestamp: 2017-08-01 11:45:00+00:00, Price: 84.9183 Timestamp: 2017-08-01 12:00:00+00:00, Price: 84.9977 Timestamp: 2017-08-01 12:15:00+00:00, Price: 84.6008 Timestamp: 2017-08-01 12:30:00+00:00, Price: 84.4421 Timestamp: 2017-08-01 12:45:00+00:00, Price: 84.204 Timestamp: 2017-08-01 13:00:00+00:00, Price: 84.3627 Timestamp: 2017-08-01 13:15:00+00:00, Price: 84.4421 Timestamp: 2017-08-01 13:30:00+00:00, Price: 84.2834 Timestamp: 2017-08-01 13:45:00+00:00, Price: 84.204 Timestamp: 2017-08-01 14:00:00+00:00, Price: 84.0453 Timestamp: 2017-08-01 14:15:00+00:00, Price: 84.0453 Timestamp: 2017-08-01 14:30:00+00:00, Price: 84.1246 Timestamp: 2017-08-01 14:45:00+00:00, Price: 85.1564 Timestamp: 2017-08-01 15:00:00+00:00, Price: 84.9183 Timestamp: 2017-08-02 06:45:00+00:00, Price: 84.7596 Timestamp: 2017-08-02 07:00:00+00:00, Price: 85.3945 Timestamp: 2017-08-02 07:15:00+00:00, Price: 85.95 Timestamp: 2017-08-02 07:30:00+00:00, Price: 86.0293 Timestamp: 2017-08-02 07:45:00+00:00, Price: 85.8706 Timestamp: 2017-08-02 08:00:00+00:00, Price: 85.8706 Timestamp: 2017-08-02 08:15:00+00:00, Price: 86.1087 Timestamp: 2017-08-02 08:30:00+00:00, Price: 86.1881 Timestamp: 2017-08-02 08:45:00+00:00, Price: 86.3468 Timestamp: 2017-08-02 09:00:00+00:00, Price: 86.5849 Timestamp: 2017-08-02 09:15:00+00:00, Price: 86.9024 Timestamp: 2017-08-02 09:30:00+00:00, Price: 86.9817 Timestamp: 2017-08-02 09:45:00+00:00, Price: 86.823 Timestamp: 2017-08-02 10:45:00+00:00, Price: 86.823 Timestamp: 2017-08-02 11:00:00+00:00, Price: 86.6643 Timestamp: 2017-08-02 11:15:00+00:00, Price: 86.4262 Timestamp: 2017-08-02 11:30:00+00:00, Price: 85.7119 Timestamp: 2017-08-02 11:45:00+00:00, Price: 85.3151 Timestamp: 2017-08-02 12:00:00+00:00, Price: 85.5532 Timestamp: 2017-08-02 12:15:00+00:00, Price: 85.5532 Timestamp: 2017-08-02 12:30:00+00:00, Price: 85.4738 Timestamp: 2017-08-02 12:45:00+00:00, Price: 85.7912 Timestamp: 2017-08-02 13:00:00+00:00, Price: 85.8706 Timestamp: 2017-08-02 13:15:00+00:00, Price: 85.7119 Timestamp: 2017-08-02 13:30:00+00:00, Price: 85.5532 Timestamp: 2017-08-02 13:45:00+00:00, Price: 85.6325 Timestamp: 2017-08-02 14:00:00+00:00, Price: 86.0293 Timestamp: 2017-08-02 14:15:00+00:00, Price: 85.95 Timestamp: 2017-08-02 14:30:00+00:00, Price: 85.7119 Timestamp: 2017-08-02 14:45:00+00:00, Price: 86.0293 Timestamp: 2017-08-02 15:00:00+00:00, Price: 85.7119
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for VAKBN in 2017/9 Timestamp: 2017-09-05 06:45:00+00:00, Price: 7.118
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for AKBNK in 2017/10 Timestamp: 2017-10-31 12:45:00+00:00, Price: 8.1925 Timestamp: 2017-10-31 13:00:00+00:00, Price: 8.1925 Timestamp: 2017-10-31 13:15:00+00:00, Price: 8.1925 Timestamp: 2017-10-31 13:30:00+00:00, Price: 8.2007 Timestamp: 2017-10-31 13:45:00+00:00, Price: 8.1925 Timestamp: 2017-10-31 14:00:00+00:00, Price: 8.1925 Timestamp: 2017-10-31 14:30:00+00:00, Price: 8.2007 Timestamp: 2017-10-31 14:45:00+00:00, Price: 8.2007 Timestamp: 2017-10-31 15:00:00+00:00, Price: 8.2336 Lower Outliers for AKBNK in 2017/10 Timestamp: 2017-10-09 06:45:00+00:00, Price: 7.518 Timestamp: 2017-10-09 07:00:00+00:00, Price: 7.3946 Timestamp: 2017-10-09 07:15:00+00:00, Price: 7.4769 Timestamp: 2017-10-09 07:30:00+00:00, Price: 7.4357 Timestamp: 2017-10-09 07:45:00+00:00, Price: 7.4439 Timestamp: 2017-10-09 08:00:00+00:00, Price: 7.4357 Timestamp: 2017-10-09 08:15:00+00:00, Price: 7.4192 Timestamp: 2017-10-09 08:30:00+00:00, Price: 7.4275 Timestamp: 2017-10-09 08:45:00+00:00, Price: 7.4192 Timestamp: 2017-10-09 09:00:00+00:00, Price: 7.4275 Timestamp: 2017-10-09 09:15:00+00:00, Price: 7.4357 Timestamp: 2017-10-09 09:30:00+00:00, Price: 7.4604 Timestamp: 2017-10-09 09:45:00+00:00, Price: 7.4769 Timestamp: 2017-10-09 10:00:00+00:00, Price: 7.4769 Timestamp: 2017-10-09 10:45:00+00:00, Price: 7.4769 Timestamp: 2017-10-09 11:00:00+00:00, Price: 7.4686 Timestamp: 2017-10-09 11:15:00+00:00, Price: 7.4933 Timestamp: 2017-10-09 11:30:00+00:00, Price: 7.485 Timestamp: 2017-10-09 11:45:00+00:00, Price: 7.5015 Timestamp: 2017-10-09 12:00:00+00:00, Price: 7.4769 Timestamp: 2017-10-09 12:15:00+00:00, Price: 7.5097 Timestamp: 2017-10-09 12:30:00+00:00, Price: 7.5097 Timestamp: 2017-10-09 12:45:00+00:00, Price: 7.518 Timestamp: 2017-10-09 13:00:00+00:00, Price: 7.518 Timestamp: 2017-10-09 13:15:00+00:00, Price: 7.518 Timestamp: 2017-10-09 13:30:00+00:00, Price: 7.518 Timestamp: 2017-10-09 13:45:00+00:00, Price: 7.518 Timestamp: 2017-10-09 14:00:00+00:00, Price: 7.5344 Timestamp: 2017-10-09 14:15:00+00:00, Price: 7.5427 Timestamp: 2017-10-09 14:30:00+00:00, Price: 7.5591 Timestamp: 2017-10-09 14:45:00+00:00, Price: 7.5508 Timestamp: 2017-10-09 15:00:00+00:00, Price: 7.5591
Upper Outliers for VAKBN in 2017/10 Timestamp: 2017-10-19 14:15:00+00:00, Price: 6.3885 Timestamp: 2017-10-19 14:30:00+00:00, Price: 6.3983 Timestamp: 2017-10-20 06:45:00+00:00, Price: 6.3786 Timestamp: 2017-10-20 07:00:00+00:00, Price: 6.3786 Timestamp: 2017-10-20 07:15:00+00:00, Price: 6.3983 Timestamp: 2017-10-20 07:30:00+00:00, Price: 6.4377 Timestamp: 2017-10-20 07:45:00+00:00, Price: 6.5068 Timestamp: 2017-10-20 08:00:00+00:00, Price: 6.5265 Timestamp: 2017-10-20 08:15:00+00:00, Price: 6.5068 Timestamp: 2017-10-20 08:30:00+00:00, Price: 6.487 Timestamp: 2017-10-20 08:45:00+00:00, Price: 6.4969 Timestamp: 2017-10-20 09:00:00+00:00, Price: 6.487 Timestamp: 2017-10-20 09:15:00+00:00, Price: 6.4969 Timestamp: 2017-10-20 09:30:00+00:00, Price: 6.487 Timestamp: 2017-10-20 09:45:00+00:00, Price: 6.4772 Timestamp: 2017-10-20 10:45:00+00:00, Price: 6.4772 Timestamp: 2017-10-20 11:00:00+00:00, Price: 6.4772 Timestamp: 2017-10-20 11:15:00+00:00, Price: 6.4772 Timestamp: 2017-10-20 11:30:00+00:00, Price: 6.4673 Timestamp: 2017-10-20 11:45:00+00:00, Price: 6.4575 Timestamp: 2017-10-20 12:00:00+00:00, Price: 6.4575 Timestamp: 2017-10-20 12:15:00+00:00, Price: 6.4476 Timestamp: 2017-10-20 12:30:00+00:00, Price: 6.418 Timestamp: 2017-10-20 13:00:00+00:00, Price: 6.3786 Timestamp: 2017-10-20 13:15:00+00:00, Price: 6.3589 Timestamp: 2017-10-20 14:45:00+00:00, Price: 6.3687 Timestamp: 2017-10-20 15:00:00+00:00, Price: 6.3589 Timestamp: 2017-10-26 13:45:00+00:00, Price: 6.3687 Lower Outliers for VAKBN in 2017/10 Timestamp: 2017-10-09 06:45:00+00:00, Price: 5.8955 Timestamp: 2017-10-09 07:00:00+00:00, Price: 5.8561 Timestamp: 2017-10-09 07:15:00+00:00, Price: 5.8758 Timestamp: 2017-10-09 07:30:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 07:45:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 08:00:00+00:00, Price: 5.8561 Timestamp: 2017-10-09 08:15:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 08:30:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 08:45:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 09:00:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 09:15:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 09:30:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 09:45:00+00:00, Price: 5.8561 Timestamp: 2017-10-09 10:00:00+00:00, Price: 5.85115 Timestamp: 2017-10-09 10:45:00+00:00, Price: 5.8462 Timestamp: 2017-10-09 11:00:00+00:00, Price: 5.8364 Timestamp: 2017-10-09 11:15:00+00:00, Price: 5.8659 Timestamp: 2017-10-09 11:30:00+00:00, Price: 5.8561 Timestamp: 2017-10-09 11:45:00+00:00, Price: 5.8857 Timestamp: 2017-10-09 12:00:00+00:00, Price: 5.8561 Timestamp: 2017-10-09 12:15:00+00:00, Price: 5.8758 Timestamp: 2017-10-09 12:30:00+00:00, Price: 5.8659 Timestamp: 2017-10-09 12:45:00+00:00, Price: 5.8758 Timestamp: 2017-10-09 13:00:00+00:00, Price: 5.8857 Timestamp: 2017-10-09 13:15:00+00:00, Price: 5.8857 Timestamp: 2017-10-09 13:30:00+00:00, Price: 5.8857 Timestamp: 2017-10-09 13:45:00+00:00, Price: 5.8659 Timestamp: 2017-10-09 14:00:00+00:00, Price: 5.8758 Timestamp: 2017-10-09 14:15:00+00:00, Price: 5.8955 Timestamp: 2017-10-09 14:30:00+00:00, Price: 5.8955 Timestamp: 2017-10-09 14:45:00+00:00, Price: 5.8857 Timestamp: 2017-10-09 15:00:00+00:00, Price: 5.8955
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Upper Outliers for VAKBN in 2017/11 Timestamp: 2017-11-01 11:00:00+00:00, Price: 6.5363 Timestamp: 2017-11-01 11:15:00+00:00, Price: 6.5462 Timestamp: 2017-11-01 11:30:00+00:00, Price: 6.5363 Timestamp: 2017-11-02 06:45:00+00:00, Price: 6.5363 Timestamp: 2017-11-02 07:00:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 07:15:00+00:00, Price: 6.5265 Timestamp: 2017-11-02 07:30:00+00:00, Price: 6.5659 Timestamp: 2017-11-02 07:45:00+00:00, Price: 6.6251 Timestamp: 2017-11-02 08:00:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 08:15:00+00:00, Price: 6.5659 Timestamp: 2017-11-02 08:30:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 08:45:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 09:00:00+00:00, Price: 6.5758 Timestamp: 2017-11-02 09:15:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 09:30:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 09:45:00+00:00, Price: 6.5758 Timestamp: 2017-11-02 10:45:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 11:00:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 11:15:00+00:00, Price: 6.5856 Timestamp: 2017-11-02 11:30:00+00:00, Price: 6.5659 Timestamp: 2017-11-02 11:45:00+00:00, Price: 6.5265 Timestamp: 2017-11-02 12:30:00+00:00, Price: 6.5363 Timestamp: 2017-11-02 12:45:00+00:00, Price: 6.5462
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Upper Outliers for TCELL in 2017/11 Timestamp: 2017-11-01 14:00:00+00:00, Price: 13.2952 Timestamp: 2017-11-01 14:30:00+00:00, Price: 13.3043 Timestamp: 2017-11-01 14:45:00+00:00, Price: 13.4123 Timestamp: 2017-11-01 15:00:00+00:00, Price: 13.4303 Timestamp: 2017-11-02 06:45:00+00:00, Price: 13.6104 Timestamp: 2017-11-02 07:00:00+00:00, Price: 13.7186 Timestamp: 2017-11-02 07:15:00+00:00, Price: 13.6014 Timestamp: 2017-11-02 07:30:00+00:00, Price: 13.6014 Timestamp: 2017-11-02 07:45:00+00:00, Price: 13.6374 Timestamp: 2017-11-02 08:00:00+00:00, Price: 13.5563 Timestamp: 2017-11-02 08:15:00+00:00, Price: 13.4753 Timestamp: 2017-11-02 08:30:00+00:00, Price: 13.4663 Timestamp: 2017-11-02 08:45:00+00:00, Price: 13.3311 Timestamp: 2017-11-02 09:00:00+00:00, Price: 13.3492 Timestamp: 2017-11-02 09:30:00+00:00, Price: 13.2861 Timestamp: 2017-11-02 09:45:00+00:00, Price: 13.3582 Timestamp: 2017-11-02 10:45:00+00:00, Price: 13.5384 Timestamp: 2017-11-02 11:00:00+00:00, Price: 13.3311 Timestamp: 2017-11-02 11:15:00+00:00, Price: 13.3131 Timestamp: 2017-11-30 11:45:00+00:00, Price: 13.3402 Timestamp: 2017-11-30 12:00:00+00:00, Price: 13.4573 Timestamp: 2017-11-30 12:15:00+00:00, Price: 13.4663 Timestamp: 2017-11-30 12:30:00+00:00, Price: 13.5294 Timestamp: 2017-11-30 12:45:00+00:00, Price: 13.5203 Timestamp: 2017-11-30 13:00:00+00:00, Price: 13.5113 Timestamp: 2017-11-30 13:15:00+00:00, Price: 13.5834 Timestamp: 2017-11-30 13:30:00+00:00, Price: 13.5384 Timestamp: 2017-11-30 13:45:00+00:00, Price: 13.5834 Timestamp: 2017-11-30 14:00:00+00:00, Price: 13.6824 Timestamp: 2017-11-30 14:15:00+00:00, Price: 13.7095 Timestamp: 2017-11-30 14:30:00+00:00, Price: 13.6284 Timestamp: 2017-11-30 14:45:00+00:00, Price: 13.6555 Timestamp: 2017-11-30 15:00:00+00:00, Price: 13.7005
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Upper Outliers for ARCLK in 2017/12 Timestamp: 2017-12-28 14:30:00+00:00, Price: 20.289 Timestamp: 2017-12-28 14:45:00+00:00, Price: 20.289 Timestamp: 2017-12-28 15:00:00+00:00, Price: 20.3276 Timestamp: 2017-12-29 06:45:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 07:00:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 07:15:00+00:00, Price: 20.4627 Timestamp: 2017-12-29 07:30:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 07:45:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 08:00:00+00:00, Price: 20.3855 Timestamp: 2017-12-29 08:15:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 08:30:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 08:45:00+00:00, Price: 20.3855 Timestamp: 2017-12-29 09:00:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 09:15:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 09:30:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 09:45:00+00:00, Price: 20.3855 Timestamp: 2017-12-29 10:00:00+00:00, Price: 20.39515 Timestamp: 2017-12-29 10:45:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 11:00:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 11:15:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 11:30:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 11:45:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 12:00:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 12:15:00+00:00, Price: 20.4048 Timestamp: 2017-12-29 12:30:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 12:45:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 13:00:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 13:15:00+00:00, Price: 20.4241 Timestamp: 2017-12-29 13:30:00+00:00, Price: 20.4434 Timestamp: 2017-12-29 13:45:00+00:00, Price: 20.5013 Timestamp: 2017-12-29 14:00:00+00:00, Price: 20.4627 Timestamp: 2017-12-29 14:15:00+00:00, Price: 20.5013 Timestamp: 2017-12-29 14:30:00+00:00, Price: 20.6751 Timestamp: 2017-12-29 14:45:00+00:00, Price: 20.7137 Timestamp: 2017-12-29 15:00:00+00:00, Price: 20.7716 Lower Outliers for ARCLK in 2017/12 Timestamp: 2017-12-01 06:45:00+00:00, Price: 19.1114 Timestamp: 2017-12-01 07:00:00+00:00, Price: 19.3045 Timestamp: 2017-12-01 07:15:00+00:00, Price: 19.3817 Timestamp: 2017-12-01 07:30:00+00:00, Price: 19.401 Timestamp: 2017-12-01 07:45:00+00:00, Price: 19.401
Upper Outliers for TUPRS in 2017/12 Timestamp: 2017-12-21 07:15:00+00:00, Price: 98.5687 Timestamp: 2017-12-21 07:30:00+00:00, Price: 99.1243 Timestamp: 2017-12-21 07:45:00+00:00, Price: 99.3624 Lower Outliers for TUPRS in 2017/12 Timestamp: 2017-12-15 07:00:00+00:00, Price: 90.0769 Timestamp: 2017-12-18 08:30:00+00:00, Price: 90.4737 Timestamp: 2017-12-18 08:45:00+00:00, Price: 90.4737 Timestamp: 2017-12-18 09:00:00+00:00, Price: 90.3943 Timestamp: 2017-12-18 09:15:00+00:00, Price: 90.2356 Timestamp: 2017-12-18 09:30:00+00:00, Price: 90.1562 Timestamp: 2017-12-18 09:45:00+00:00, Price: 90.0769 Timestamp: 2017-12-18 10:00:00+00:00, Price: 90.0769 Timestamp: 2017-12-18 10:45:00+00:00, Price: 90.0769 Timestamp: 2017-12-18 11:00:00+00:00, Price: 89.9975 Timestamp: 2017-12-18 11:15:00+00:00, Price: 89.1245 Timestamp: 2017-12-18 11:30:00+00:00, Price: 89.6007 Timestamp: 2017-12-18 11:45:00+00:00, Price: 89.7594 Timestamp: 2017-12-18 12:00:00+00:00, Price: 90.315 Timestamp: 2017-12-18 12:15:00+00:00, Price: 90.1562 Timestamp: 2017-12-18 12:30:00+00:00, Price: 90.3943 Timestamp: 2017-12-18 12:45:00+00:00, Price: 90.3943 Timestamp: 2017-12-18 13:00:00+00:00, Price: 90.3943 Timestamp: 2017-12-18 13:15:00+00:00, Price: 90.4737 Timestamp: 2017-12-18 13:30:00+00:00, Price: 90.3943 Timestamp: 2017-12-18 13:45:00+00:00, Price: 89.9975 Timestamp: 2017-12-18 14:00:00+00:00, Price: 89.9181 Timestamp: 2017-12-18 14:15:00+00:00, Price: 89.2038 Timestamp: 2017-12-18 14:30:00+00:00, Price: 89.4419 Timestamp: 2017-12-18 14:45:00+00:00, Price: 89.3626 Timestamp: 2017-12-18 15:00:00+00:00, Price: 89.2038 Timestamp: 2017-12-19 06:45:00+00:00, Price: 89.3626 Timestamp: 2017-12-19 07:00:00+00:00, Price: 89.3626 Timestamp: 2017-12-19 07:15:00+00:00, Price: 88.9658 Timestamp: 2017-12-19 07:30:00+00:00, Price: 89.0451 Timestamp: 2017-12-19 07:45:00+00:00, Price: 88.9658 Timestamp: 2017-12-19 08:00:00+00:00, Price: 90.0769 Timestamp: 2017-12-19 08:30:00+00:00, Price: 90.4737
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Upper Outliers for AKBNK in 2018/2 Timestamp: 2018-02-01 09:45:00+00:00, Price: 9.0397 Timestamp: 2018-02-01 10:45:00+00:00, Price: 9.0397 Lower Outliers for AKBNK in 2018/2 Timestamp: 2018-02-09 07:15:00+00:00, Price: 8.2665 Timestamp: 2018-02-09 07:45:00+00:00, Price: 8.2583 Timestamp: 2018-02-09 12:30:00+00:00, Price: 8.2665 Timestamp: 2018-02-09 12:45:00+00:00, Price: 8.2336 Timestamp: 2018-02-09 13:00:00+00:00, Price: 8.2665
Upper Outliers for VAKBN in 2018/2 Timestamp: 2018-02-01 07:30:00+00:00, Price: 7.5222 Timestamp: 2018-02-01 07:45:00+00:00, Price: 7.5321 Timestamp: 2018-02-01 08:00:00+00:00, Price: 7.5025 Timestamp: 2018-02-01 08:15:00+00:00, Price: 7.4828 Timestamp: 2018-02-01 08:30:00+00:00, Price: 7.5123 Timestamp: 2018-02-01 08:45:00+00:00, Price: 7.4828 Timestamp: 2018-02-01 09:00:00+00:00, Price: 7.4828 Timestamp: 2018-02-01 09:15:00+00:00, Price: 7.4828
Upper Outliers for ARCLK in 2018/2 Timestamp: 2018-02-01 12:45:00+00:00, Price: 18.8508 Timestamp: 2018-02-01 13:00:00+00:00, Price: 18.8412 Timestamp: 2018-02-01 13:15:00+00:00, Price: 18.8412 Timestamp: 2018-02-05 08:30:00+00:00, Price: 18.9377 Timestamp: 2018-02-05 08:45:00+00:00, Price: 18.957 Timestamp: 2018-02-05 09:00:00+00:00, Price: 18.9859 Timestamp: 2018-02-05 09:15:00+00:00, Price: 18.9859 Timestamp: 2018-02-05 09:30:00+00:00, Price: 18.9377 Timestamp: 2018-02-05 09:45:00+00:00, Price: 18.8508 Timestamp: 2018-02-05 10:45:00+00:00, Price: 18.8508 Timestamp: 2018-02-05 11:00:00+00:00, Price: 18.8218 Timestamp: 2018-02-05 11:15:00+00:00, Price: 18.8508 Timestamp: 2018-02-05 11:30:00+00:00, Price: 18.8991 Timestamp: 2018-02-05 11:45:00+00:00, Price: 18.9184 Timestamp: 2018-02-05 12:00:00+00:00, Price: 18.8508 Timestamp: 2018-02-05 12:15:00+00:00, Price: 18.8412
Lower Outliers for TUPRS in 2018/2 Timestamp: 2018-02-06 06:45:00+00:00, Price: 84.9183
Upper Outliers for TCELL in 2018/2 Timestamp: 2018-02-01 07:00:00+00:00, Price: 14.6434 Timestamp: 2018-02-01 07:15:00+00:00, Price: 14.6434 Timestamp: 2018-02-01 08:30:00+00:00, Price: 14.6248 Timestamp: 2018-02-01 09:45:00+00:00, Price: 14.6248 Timestamp: 2018-02-01 10:45:00+00:00, Price: 14.634 Lower Outliers for TCELL in 2018/2 Timestamp: 2018-02-20 14:45:00+00:00, Price: 13.5483 Timestamp: 2018-02-20 15:00:00+00:00, Price: 13.5576 Timestamp: 2018-02-21 13:45:00+00:00, Price: 13.7153
Upper Outliers for THYAO in 2018/2 Timestamp: 2018-02-26 14:45:00+00:00, Price: 18.89 Timestamp: 2018-02-26 15:00:00+00:00, Price: 18.92 Timestamp: 2018-02-27 06:45:00+00:00, Price: 18.92 Timestamp: 2018-02-27 09:45:00+00:00, Price: 18.85 Timestamp: 2018-02-27 10:45:00+00:00, Price: 18.84 Timestamp: 2018-02-27 11:00:00+00:00, Price: 18.83 Timestamp: 2018-02-28 09:00:00+00:00, Price: 18.85 Timestamp: 2018-02-28 09:15:00+00:00, Price: 18.84 Timestamp: 2018-02-28 09:30:00+00:00, Price: 18.84 Timestamp: 2018-02-28 09:45:00+00:00, Price: 18.86 Timestamp: 2018-02-28 10:00:00+00:00, Price: 18.855 Timestamp: 2018-02-28 10:45:00+00:00, Price: 18.85 Timestamp: 2018-02-28 11:00:00+00:00, Price: 18.85 Timestamp: 2018-02-28 11:15:00+00:00, Price: 18.83 Timestamp: 2018-02-28 12:15:00+00:00, Price: 18.91 Timestamp: 2018-02-28 12:30:00+00:00, Price: 18.9 Timestamp: 2018-02-28 12:45:00+00:00, Price: 18.91 Timestamp: 2018-02-28 13:00:00+00:00, Price: 18.92 Timestamp: 2018-02-28 13:15:00+00:00, Price: 18.94 Timestamp: 2018-02-28 13:30:00+00:00, Price: 18.95 Timestamp: 2018-02-28 13:45:00+00:00, Price: 18.96 Timestamp: 2018-02-28 14:00:00+00:00, Price: 18.97 Timestamp: 2018-02-28 14:15:00+00:00, Price: 19.02 Timestamp: 2018-02-28 14:30:00+00:00, Price: 19.0 Timestamp: 2018-02-28 14:45:00+00:00, Price: 19.01 Timestamp: 2018-02-28 15:00:00+00:00, Price: 19.07
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Upper Outliers for ARCLK in 2018/5 Timestamp: 2018-05-02 06:45:00+00:00, Price: 18.08 Timestamp: 2018-05-02 07:00:00+00:00, Price: 18.35 Timestamp: 2018-05-02 07:15:00+00:00, Price: 18.67 Timestamp: 2018-05-02 07:30:00+00:00, Price: 18.7 Timestamp: 2018-05-02 07:45:00+00:00, Price: 18.57 Timestamp: 2018-05-02 08:00:00+00:00, Price: 18.65 Timestamp: 2018-05-02 08:15:00+00:00, Price: 18.57 Timestamp: 2018-05-02 08:30:00+00:00, Price: 18.38 Timestamp: 2018-05-02 08:45:00+00:00, Price: 18.49 Timestamp: 2018-05-02 09:00:00+00:00, Price: 18.27 Timestamp: 2018-05-02 09:15:00+00:00, Price: 18.35 Timestamp: 2018-05-02 09:30:00+00:00, Price: 18.33 Timestamp: 2018-05-02 09:45:00+00:00, Price: 18.33 Timestamp: 2018-05-02 10:00:00+00:00, Price: 18.29 Timestamp: 2018-05-02 10:45:00+00:00, Price: 18.25 Timestamp: 2018-05-02 11:00:00+00:00, Price: 18.12 Timestamp: 2018-05-02 11:15:00+00:00, Price: 18.22 Timestamp: 2018-05-02 11:30:00+00:00, Price: 18.45 Timestamp: 2018-05-02 11:45:00+00:00, Price: 18.42 Timestamp: 2018-05-02 12:00:00+00:00, Price: 18.46 Timestamp: 2018-05-02 12:15:00+00:00, Price: 18.41 Timestamp: 2018-05-02 12:30:00+00:00, Price: 18.41 Timestamp: 2018-05-02 12:45:00+00:00, Price: 18.41 Timestamp: 2018-05-02 13:00:00+00:00, Price: 18.43 Timestamp: 2018-05-02 13:15:00+00:00, Price: 18.35 Timestamp: 2018-05-02 13:30:00+00:00, Price: 18.22 Timestamp: 2018-05-02 13:45:00+00:00, Price: 18.17 Timestamp: 2018-05-02 14:00:00+00:00, Price: 18.18 Timestamp: 2018-05-02 14:15:00+00:00, Price: 18.17 Timestamp: 2018-05-02 14:30:00+00:00, Price: 18.16 Timestamp: 2018-05-02 14:45:00+00:00, Price: 18.29 Timestamp: 2018-05-02 15:00:00+00:00, Price: 18.32 Timestamp: 2018-05-03 06:45:00+00:00, Price: 18.32 Timestamp: 2018-05-03 07:00:00+00:00, Price: 18.17 Timestamp: 2018-05-03 07:15:00+00:00, Price: 18.05 Timestamp: 2018-05-03 07:30:00+00:00, Price: 18.01 Timestamp: 2018-05-03 07:45:00+00:00, Price: 18.04 Timestamp: 2018-05-03 08:00:00+00:00, Price: 18.1 Timestamp: 2018-05-03 08:15:00+00:00, Price: 18.06 Timestamp: 2018-05-03 08:30:00+00:00, Price: 18.05 Timestamp: 2018-05-03 08:45:00+00:00, Price: 17.97 Timestamp: 2018-05-03 09:00:00+00:00, Price: 18.01 Timestamp: 2018-05-03 09:15:00+00:00, Price: 18.01 Timestamp: 2018-05-03 09:30:00+00:00, Price: 18.03 Timestamp: 2018-05-03 09:45:00+00:00, Price: 18.02 Timestamp: 2018-05-03 10:00:00+00:00, Price: 18.02 Timestamp: 2018-05-03 10:45:00+00:00, Price: 18.02 Timestamp: 2018-05-03 11:00:00+00:00, Price: 18.03 Timestamp: 2018-05-03 11:15:00+00:00, Price: 17.99 Timestamp: 2018-05-03 11:30:00+00:00, Price: 18.03 Timestamp: 2018-05-03 11:45:00+00:00, Price: 18.03 Timestamp: 2018-05-03 12:00:00+00:00, Price: 18.02 Timestamp: 2018-05-03 12:15:00+00:00, Price: 17.99 Timestamp: 2018-05-03 12:30:00+00:00, Price: 18.03 Timestamp: 2018-05-03 12:45:00+00:00, Price: 18.01 Timestamp: 2018-05-03 13:00:00+00:00, Price: 18.1 Timestamp: 2018-05-03 13:15:00+00:00, Price: 18.2 Timestamp: 2018-05-03 13:30:00+00:00, Price: 18.2 Timestamp: 2018-05-03 13:45:00+00:00, Price: 18.19 Timestamp: 2018-05-03 14:00:00+00:00, Price: 17.97 Timestamp: 2018-05-04 07:00:00+00:00, Price: 17.97
Upper Outliers for TUPRS in 2018/5 Timestamp: 2018-05-29 15:00:00+00:00, Price: 96.5579 Lower Outliers for TUPRS in 2018/5 Timestamp: 2018-05-10 06:45:00+00:00, Price: 84.0411 Timestamp: 2018-05-10 07:00:00+00:00, Price: 84.354 Timestamp: 2018-05-10 07:15:00+00:00, Price: 85.0246 Timestamp: 2018-05-10 07:30:00+00:00, Price: 84.4435 Timestamp: 2018-05-10 07:45:00+00:00, Price: 83.5047 Timestamp: 2018-05-10 08:00:00+00:00, Price: 83.5047 Timestamp: 2018-05-10 08:15:00+00:00, Price: 83.9964 Timestamp: 2018-05-10 08:30:00+00:00, Price: 84.0858 Timestamp: 2018-05-10 08:45:00+00:00, Price: 83.8623 Timestamp: 2018-05-10 09:00:00+00:00, Price: 83.9517 Timestamp: 2018-05-10 09:15:00+00:00, Price: 84.0411 Timestamp: 2018-05-10 09:30:00+00:00, Price: 84.354 Timestamp: 2018-05-10 09:45:00+00:00, Price: 84.354 Timestamp: 2018-05-10 10:45:00+00:00, Price: 84.5776
Upper Outliers for TCELL in 2018/5 Timestamp: 2018-05-02 06:45:00+00:00, Price: 12.9823 Timestamp: 2018-05-02 07:00:00+00:00, Price: 12.8987 Timestamp: 2018-05-02 07:15:00+00:00, Price: 12.9173 Timestamp: 2018-05-02 07:30:00+00:00, Price: 12.8338 Timestamp: 2018-05-02 07:45:00+00:00, Price: 12.8152 Timestamp: 2018-05-02 08:00:00+00:00, Price: 12.8616 Timestamp: 2018-05-02 08:15:00+00:00, Price: 12.908 Timestamp: 2018-05-02 08:30:00+00:00, Price: 12.8431 Timestamp: 2018-05-02 08:45:00+00:00, Price: 12.8245 Timestamp: 2018-05-02 09:00:00+00:00, Price: 12.8524 Timestamp: 2018-05-02 09:15:00+00:00, Price: 12.8524 Timestamp: 2018-05-02 09:30:00+00:00, Price: 12.8802 Timestamp: 2018-05-02 09:45:00+00:00, Price: 12.8709 Timestamp: 2018-05-02 10:00:00+00:00, Price: 12.843050000000002 Timestamp: 2018-05-02 10:45:00+00:00, Price: 12.8152 Timestamp: 2018-05-02 11:00:00+00:00, Price: 12.7874 Timestamp: 2018-05-02 11:15:00+00:00, Price: 12.806 Timestamp: 2018-05-02 11:30:00+00:00, Price: 12.6853 Timestamp: 2018-05-02 11:45:00+00:00, Price: 12.6017 Timestamp: 2018-05-02 12:00:00+00:00, Price: 12.6203 Timestamp: 2018-05-02 12:15:00+00:00, Price: 12.6203 Timestamp: 2018-05-02 12:30:00+00:00, Price: 12.5646 Timestamp: 2018-05-02 12:45:00+00:00, Price: 12.6203 Timestamp: 2018-05-02 13:00:00+00:00, Price: 12.6389 Timestamp: 2018-05-02 13:15:00+00:00, Price: 12.6111 Timestamp: 2018-05-02 13:30:00+00:00, Price: 12.6111 Timestamp: 2018-05-02 13:45:00+00:00, Price: 12.5555 Timestamp: 2018-05-02 14:00:00+00:00, Price: 12.6111 Timestamp: 2018-05-02 14:15:00+00:00, Price: 12.6389 Timestamp: 2018-05-02 14:30:00+00:00, Price: 12.6389 Timestamp: 2018-05-02 14:45:00+00:00, Price: 12.6111 Timestamp: 2018-05-02 15:00:00+00:00, Price: 12.6017 Timestamp: 2018-05-03 06:45:00+00:00, Price: 12.6297 Timestamp: 2018-05-03 07:00:00+00:00, Price: 12.6111 Timestamp: 2018-05-03 07:15:00+00:00, Price: 12.6111 Timestamp: 2018-05-03 07:30:00+00:00, Price: 12.6482 Timestamp: 2018-05-03 07:45:00+00:00, Price: 12.6389 Timestamp: 2018-05-03 08:00:00+00:00, Price: 12.6668 Timestamp: 2018-05-03 08:15:00+00:00, Price: 12.6668 Timestamp: 2018-05-03 08:30:00+00:00, Price: 12.6668 Timestamp: 2018-05-03 08:45:00+00:00, Price: 12.6017 Timestamp: 2018-05-03 09:00:00+00:00, Price: 12.5832 Timestamp: 2018-05-03 09:15:00+00:00, Price: 12.6203 Timestamp: 2018-05-03 09:30:00+00:00, Price: 12.6017 Timestamp: 2018-05-03 09:45:00+00:00, Price: 12.6017 Timestamp: 2018-05-03 10:00:00+00:00, Price: 12.6017 Timestamp: 2018-05-03 10:45:00+00:00, Price: 12.6017 Timestamp: 2018-05-03 11:15:00+00:00, Price: 12.5555 Timestamp: 2018-05-03 11:30:00+00:00, Price: 12.574 Timestamp: 2018-05-11 06:45:00+00:00, Price: 12.6017 Lower Outliers for TCELL in 2018/5 Timestamp: 2018-05-31 08:15:00+00:00, Price: 11.5068 Timestamp: 2018-05-31 08:30:00+00:00, Price: 11.414 Timestamp: 2018-05-31 08:45:00+00:00, Price: 11.4975 Timestamp: 2018-05-31 09:00:00+00:00, Price: 11.4604 Timestamp: 2018-05-31 09:15:00+00:00, Price: 11.4326 Timestamp: 2018-05-31 09:30:00+00:00, Price: 11.414 Timestamp: 2018-05-31 09:45:00+00:00, Price: 11.414 Timestamp: 2018-05-31 10:00:00+00:00, Price: 11.41865 Timestamp: 2018-05-31 10:45:00+00:00, Price: 11.4233 Timestamp: 2018-05-31 11:00:00+00:00, Price: 11.414 Timestamp: 2018-05-31 11:15:00+00:00, Price: 11.3398 Timestamp: 2018-05-31 11:30:00+00:00, Price: 11.3583 Timestamp: 2018-05-31 11:45:00+00:00, Price: 11.3305 Timestamp: 2018-05-31 12:00:00+00:00, Price: 11.3491 Timestamp: 2018-05-31 12:15:00+00:00, Price: 11.3211 Timestamp: 2018-05-31 12:30:00+00:00, Price: 11.3491 Timestamp: 2018-05-31 12:45:00+00:00, Price: 11.3398 Timestamp: 2018-05-31 13:00:00+00:00, Price: 11.3769 Timestamp: 2018-05-31 13:15:00+00:00, Price: 11.3769 Timestamp: 2018-05-31 13:30:00+00:00, Price: 11.3398 Timestamp: 2018-05-31 13:45:00+00:00, Price: 11.3026 Timestamp: 2018-05-31 14:00:00+00:00, Price: 11.2655 Timestamp: 2018-05-31 14:15:00+00:00, Price: 11.1821 Timestamp: 2018-05-31 14:30:00+00:00, Price: 11.2006 Timestamp: 2018-05-31 14:45:00+00:00, Price: 11.2563 Timestamp: 2018-05-31 15:00:00+00:00, Price: 11.1356
Upper Outliers for THYAO in 2018/5 Timestamp: 2018-05-28 11:00:00+00:00, Price: 17.41 Timestamp: 2018-05-28 11:15:00+00:00, Price: 17.43 Timestamp: 2018-05-28 12:30:00+00:00, Price: 17.41 Timestamp: 2018-05-28 13:15:00+00:00, Price: 17.42 Timestamp: 2018-05-28 14:30:00+00:00, Price: 17.41 Timestamp: 2018-05-28 14:45:00+00:00, Price: 17.53 Timestamp: 2018-05-28 15:00:00+00:00, Price: 17.55 Timestamp: 2018-05-29 06:45:00+00:00, Price: 17.53 Timestamp: 2018-05-29 14:00:00+00:00, Price: 17.55 Timestamp: 2018-05-29 14:15:00+00:00, Price: 17.5 Timestamp: 2018-05-29 14:30:00+00:00, Price: 17.47 Timestamp: 2018-05-29 14:45:00+00:00, Price: 17.49 Timestamp: 2018-05-29 15:00:00+00:00, Price: 17.46 Timestamp: 2018-05-30 06:45:00+00:00, Price: 17.48 Timestamp: 2018-05-30 07:00:00+00:00, Price: 17.51 Timestamp: 2018-05-30 07:15:00+00:00, Price: 17.49 Timestamp: 2018-05-30 07:30:00+00:00, Price: 17.56 Timestamp: 2018-05-30 07:45:00+00:00, Price: 17.58 Timestamp: 2018-05-30 08:00:00+00:00, Price: 17.56 Timestamp: 2018-05-30 08:15:00+00:00, Price: 17.54 Timestamp: 2018-05-30 08:30:00+00:00, Price: 17.5 Timestamp: 2018-05-30 08:45:00+00:00, Price: 17.51 Timestamp: 2018-05-30 09:00:00+00:00, Price: 17.51 Timestamp: 2018-05-30 09:15:00+00:00, Price: 17.51 Timestamp: 2018-05-30 09:30:00+00:00, Price: 17.49 Timestamp: 2018-05-30 09:45:00+00:00, Price: 17.48 Timestamp: 2018-05-30 10:45:00+00:00, Price: 17.49 Timestamp: 2018-05-30 11:00:00+00:00, Price: 17.48 Timestamp: 2018-05-30 11:30:00+00:00, Price: 17.42 Timestamp: 2018-05-30 11:45:00+00:00, Price: 17.42 Timestamp: 2018-05-30 12:00:00+00:00, Price: 17.45 Timestamp: 2018-05-30 12:15:00+00:00, Price: 17.45 Timestamp: 2018-05-30 12:30:00+00:00, Price: 17.46 Timestamp: 2018-05-30 12:45:00+00:00, Price: 17.51 Timestamp: 2018-05-30 13:00:00+00:00, Price: 17.42 Timestamp: 2018-05-30 13:15:00+00:00, Price: 17.42 Timestamp: 2018-05-31 06:45:00+00:00, Price: 17.41 Timestamp: 2018-05-31 07:15:00+00:00, Price: 17.42 Timestamp: 2018-05-31 07:45:00+00:00, Price: 17.42 Timestamp: 2018-05-31 08:00:00+00:00, Price: 17.46 Timestamp: 2018-05-31 08:15:00+00:00, Price: 17.47 Lower Outliers for THYAO in 2018/5 Timestamp: 2018-05-09 07:30:00+00:00, Price: 15.6 Timestamp: 2018-05-09 08:00:00+00:00, Price: 15.93 Timestamp: 2018-05-24 13:45:00+00:00, Price: 15.79 Timestamp: 2018-05-24 14:15:00+00:00, Price: 15.89 Timestamp: 2018-05-24 14:30:00+00:00, Price: 15.76 Timestamp: 2018-05-24 14:45:00+00:00, Price: 15.66 Timestamp: 2018-05-24 15:00:00+00:00, Price: 15.66 Timestamp: 2018-05-25 06:45:00+00:00, Price: 15.95 Timestamp: 2018-05-25 07:00:00+00:00, Price: 15.84 Timestamp: 2018-05-25 07:45:00+00:00, Price: 15.91 Timestamp: 2018-05-25 08:00:00+00:00, Price: 15.87 Timestamp: 2018-05-25 08:30:00+00:00, Price: 15.94 Timestamp: 2018-05-25 09:00:00+00:00, Price: 15.9 Timestamp: 2018-05-25 09:15:00+00:00, Price: 15.85 Timestamp: 2018-05-25 09:30:00+00:00, Price: 15.86 Timestamp: 2018-05-25 09:45:00+00:00, Price: 15.85 Timestamp: 2018-05-25 10:00:00+00:00, Price: 15.86 Timestamp: 2018-05-25 10:45:00+00:00, Price: 15.87
Upper Outliers for AKBNK in 2018/6 Timestamp: 2018-06-01 06:45:00+00:00, Price: 6.8976 Timestamp: 2018-06-01 07:00:00+00:00, Price: 6.8633 Timestamp: 2018-06-04 07:45:00+00:00, Price: 6.7947 Timestamp: 2018-06-04 08:45:00+00:00, Price: 6.7861 Timestamp: 2018-06-04 09:15:00+00:00, Price: 6.8461 Timestamp: 2018-06-04 09:30:00+00:00, Price: 6.8204 Timestamp: 2018-06-04 11:30:00+00:00, Price: 6.7861
NO OUTLIERS
Upper Outliers for ARCLK in 2018/6 Timestamp: 2018-06-01 06:45:00+00:00, Price: 16.62 Timestamp: 2018-06-01 07:00:00+00:00, Price: 16.42 Timestamp: 2018-06-01 09:15:00+00:00, Price: 16.44 Timestamp: 2018-06-01 09:30:00+00:00, Price: 16.45 Timestamp: 2018-06-01 09:45:00+00:00, Price: 16.45 Timestamp: 2018-06-01 10:45:00+00:00, Price: 16.55 Timestamp: 2018-06-01 11:00:00+00:00, Price: 16.5 Timestamp: 2018-06-01 11:30:00+00:00, Price: 16.42 Timestamp: 2018-06-01 12:30:00+00:00, Price: 16.42 Timestamp: 2018-06-01 12:45:00+00:00, Price: 16.48 Timestamp: 2018-06-01 13:00:00+00:00, Price: 16.48 Timestamp: 2018-06-01 13:30:00+00:00, Price: 16.46 Timestamp: 2018-06-01 13:45:00+00:00, Price: 16.49 Timestamp: 2018-06-01 14:00:00+00:00, Price: 16.46 Timestamp: 2018-06-04 07:00:00+00:00, Price: 16.5 Timestamp: 2018-06-04 07:15:00+00:00, Price: 16.46 Timestamp: 2018-06-04 07:30:00+00:00, Price: 16.5 Timestamp: 2018-06-04 07:45:00+00:00, Price: 16.53 Timestamp: 2018-06-04 08:00:00+00:00, Price: 16.51 Timestamp: 2018-06-04 08:15:00+00:00, Price: 16.48 Timestamp: 2018-06-04 08:30:00+00:00, Price: 16.51 Timestamp: 2018-06-04 08:45:00+00:00, Price: 16.53 Timestamp: 2018-06-04 09:00:00+00:00, Price: 16.54 Timestamp: 2018-06-04 09:15:00+00:00, Price: 16.54 Timestamp: 2018-06-04 09:30:00+00:00, Price: 16.51 Timestamp: 2018-06-04 09:45:00+00:00, Price: 16.43 Timestamp: 2018-06-04 10:00:00+00:00, Price: 16.43 Timestamp: 2018-06-04 10:45:00+00:00, Price: 16.43 Timestamp: 2018-06-04 11:30:00+00:00, Price: 16.44 Timestamp: 2018-06-04 11:45:00+00:00, Price: 16.43 Timestamp: 2018-06-22 06:45:00+00:00, Price: 16.5 Timestamp: 2018-06-25 06:45:00+00:00, Price: 16.5 Timestamp: 2018-06-25 07:00:00+00:00, Price: 16.5
NO OUTLIERS
Upper Outliers for TCELL in 2018/6 Timestamp: 2018-06-28 09:15:00+00:00, Price: 11.6377 Timestamp: 2018-06-28 09:45:00+00:00, Price: 11.6471 Timestamp: 2018-06-28 10:45:00+00:00, Price: 11.6567 Timestamp: 2018-06-28 11:00:00+00:00, Price: 11.6662 Timestamp: 2018-06-28 11:15:00+00:00, Price: 11.6377 Timestamp: 2018-06-28 13:00:00+00:00, Price: 11.7138 Timestamp: 2018-06-28 13:15:00+00:00, Price: 11.7613 Timestamp: 2018-06-28 13:30:00+00:00, Price: 11.7518 Timestamp: 2018-06-28 13:45:00+00:00, Price: 11.7233 Timestamp: 2018-06-28 14:00:00+00:00, Price: 11.7613 Timestamp: 2018-06-28 14:15:00+00:00, Price: 11.7233 Timestamp: 2018-06-28 14:30:00+00:00, Price: 11.7518 Timestamp: 2018-06-28 14:45:00+00:00, Price: 11.6947 Timestamp: 2018-06-28 15:00:00+00:00, Price: 11.7138 Timestamp: 2018-06-29 06:45:00+00:00, Price: 11.7994 Timestamp: 2018-06-29 07:00:00+00:00, Price: 11.9136 Timestamp: 2018-06-29 07:15:00+00:00, Price: 11.9041 Timestamp: 2018-06-29 07:30:00+00:00, Price: 11.8851 Timestamp: 2018-06-29 07:45:00+00:00, Price: 11.8565 Timestamp: 2018-06-29 08:00:00+00:00, Price: 11.8089 Timestamp: 2018-06-29 08:15:00+00:00, Price: 11.7994 Timestamp: 2018-06-29 08:30:00+00:00, Price: 11.7709 Timestamp: 2018-06-29 08:45:00+00:00, Price: 11.7613 Timestamp: 2018-06-29 09:00:00+00:00, Price: 11.7518 Timestamp: 2018-06-29 09:15:00+00:00, Price: 11.7994 Timestamp: 2018-06-29 09:30:00+00:00, Price: 11.7994 Timestamp: 2018-06-29 09:45:00+00:00, Price: 11.7899 Timestamp: 2018-06-29 10:45:00+00:00, Price: 11.8185 Timestamp: 2018-06-29 11:00:00+00:00, Price: 11.8279 Timestamp: 2018-06-29 11:15:00+00:00, Price: 11.8185 Timestamp: 2018-06-29 11:30:00+00:00, Price: 11.8851 Timestamp: 2018-06-29 11:45:00+00:00, Price: 11.8755 Timestamp: 2018-06-29 12:00:00+00:00, Price: 11.8945 Timestamp: 2018-06-29 12:15:00+00:00, Price: 11.8565 Timestamp: 2018-06-29 12:30:00+00:00, Price: 11.8375 Timestamp: 2018-06-29 12:45:00+00:00, Price: 11.7709 Timestamp: 2018-06-29 13:00:00+00:00, Price: 11.7709 Timestamp: 2018-06-29 13:15:00+00:00, Price: 11.7518 Timestamp: 2018-06-29 13:30:00+00:00, Price: 11.7043 Timestamp: 2018-06-29 13:45:00+00:00, Price: 11.6757 Timestamp: 2018-06-29 14:00:00+00:00, Price: 11.6377
NO OUTLIERS
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NO OUTLIERS
Lower Outliers for THYAO in 2018/8 Timestamp: 2018-08-17 07:00:00+00:00, Price: 15.01 Timestamp: 2018-08-17 07:15:00+00:00, Price: 14.65 Timestamp: 2018-08-17 07:30:00+00:00, Price: 14.35 Timestamp: 2018-08-17 07:45:00+00:00, Price: 14.48 Timestamp: 2018-08-17 08:00:00+00:00, Price: 14.72 Timestamp: 2018-08-17 08:15:00+00:00, Price: 14.67 Timestamp: 2018-08-17 08:30:00+00:00, Price: 14.62 Timestamp: 2018-08-17 08:45:00+00:00, Price: 14.57 Timestamp: 2018-08-17 09:00:00+00:00, Price: 14.6 Timestamp: 2018-08-17 09:15:00+00:00, Price: 14.52 Timestamp: 2018-08-17 09:30:00+00:00, Price: 14.58 Timestamp: 2018-08-17 09:45:00+00:00, Price: 14.57 Timestamp: 2018-08-17 10:00:00+00:00, Price: 14.58 Timestamp: 2018-08-17 10:45:00+00:00, Price: 14.59 Timestamp: 2018-08-17 11:00:00+00:00, Price: 14.91 Timestamp: 2018-08-17 11:15:00+00:00, Price: 15.11 Timestamp: 2018-08-17 11:30:00+00:00, Price: 15.09 Timestamp: 2018-08-17 12:00:00+00:00, Price: 15.07 Timestamp: 2018-08-17 12:15:00+00:00, Price: 15.0 Timestamp: 2018-08-17 12:30:00+00:00, Price: 15.06 Timestamp: 2018-08-17 12:45:00+00:00, Price: 15.01 Timestamp: 2018-08-17 13:00:00+00:00, Price: 14.97 Timestamp: 2018-08-17 13:15:00+00:00, Price: 14.96 Timestamp: 2018-08-17 13:30:00+00:00, Price: 15.09 Timestamp: 2018-08-17 14:15:00+00:00, Price: 15.11 Timestamp: 2018-08-17 14:30:00+00:00, Price: 15.09
NO OUTLIERS
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NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Lower Outliers for TUPRS in 2018/10 Timestamp: 2018-10-30 11:00:00+00:00, Price: 109.9687 Timestamp: 2018-10-30 11:15:00+00:00, Price: 109.3429 Timestamp: 2018-10-30 11:30:00+00:00, Price: 109.5217 Timestamp: 2018-10-30 11:45:00+00:00, Price: 109.6111 Timestamp: 2018-10-30 12:00:00+00:00, Price: 109.5217 Timestamp: 2018-10-30 12:15:00+00:00, Price: 109.5217 Timestamp: 2018-10-30 12:30:00+00:00, Price: 109.8793 Timestamp: 2018-10-30 12:45:00+00:00, Price: 109.6111 Timestamp: 2018-10-30 13:00:00+00:00, Price: 109.7005 Timestamp: 2018-10-30 13:15:00+00:00, Price: 109.9687 Timestamp: 2018-10-30 13:30:00+00:00, Price: 109.7005 Timestamp: 2018-10-30 13:45:00+00:00, Price: 110.1475
NO OUTLIERS
NO OUTLIERS
Lower Outliers for AKBNK in 2018/11 Timestamp: 2018-11-01 06:45:00+00:00, Price: 5.6451 Timestamp: 2018-11-01 07:00:00+00:00, Price: 5.6365 Timestamp: 2018-11-01 07:15:00+00:00, Price: 5.5679 Timestamp: 2018-11-01 07:30:00+00:00, Price: 5.5507 Timestamp: 2018-11-01 07:45:00+00:00, Price: 5.5679 Timestamp: 2018-11-01 08:00:00+00:00, Price: 5.6193 Timestamp: 2018-11-01 08:15:00+00:00, Price: 5.6365 Timestamp: 2018-11-01 08:30:00+00:00, Price: 5.6536 Timestamp: 2018-11-01 08:45:00+00:00, Price: 5.7309 Timestamp: 2018-11-01 09:00:00+00:00, Price: 5.7223 Timestamp: 2018-11-01 09:15:00+00:00, Price: 5.7309 Timestamp: 2018-11-01 09:30:00+00:00, Price: 5.7394 Timestamp: 2018-11-01 09:45:00+00:00, Price: 5.7823 Timestamp: 2018-11-01 10:00:00+00:00, Price: 5.7823 Timestamp: 2018-11-01 10:45:00+00:00, Price: 5.7823 Timestamp: 2018-11-01 11:00:00+00:00, Price: 5.7652 Timestamp: 2018-11-01 11:15:00+00:00, Price: 5.7394 Timestamp: 2018-11-01 11:30:00+00:00, Price: 5.7652 Timestamp: 2018-11-01 11:45:00+00:00, Price: 5.7909 Timestamp: 2018-11-01 12:00:00+00:00, Price: 5.7995 Timestamp: 2018-11-01 12:15:00+00:00, Price: 5.7823 Timestamp: 2018-11-01 12:30:00+00:00, Price: 5.7309 Timestamp: 2018-11-01 12:45:00+00:00, Price: 5.7566 Timestamp: 2018-11-01 13:00:00+00:00, Price: 5.8252 Timestamp: 2018-11-01 13:15:00+00:00, Price: 5.8252 Timestamp: 2018-11-01 13:30:00+00:00, Price: 5.8081 Timestamp: 2018-11-01 13:45:00+00:00, Price: 5.8081 Timestamp: 2018-11-01 14:00:00+00:00, Price: 5.8424
Upper Outliers for VAKBN in 2018/11 Timestamp: 2018-11-30 14:45:00+00:00, Price: 4.03 Lower Outliers for VAKBN in 2018/11 Timestamp: 2018-11-01 07:00:00+00:00, Price: 3.42 Timestamp: 2018-11-01 07:30:00+00:00, Price: 3.42 Timestamp: 2018-11-01 07:45:00+00:00, Price: 3.42 Timestamp: 2018-11-01 08:00:00+00:00, Price: 3.42
NO OUTLIERS
Upper Outliers for TUPRS in 2018/11 Timestamp: 2018-11-05 11:15:00+00:00, Price: 122.3067 Timestamp: 2018-11-05 11:45:00+00:00, Price: 122.6643 Timestamp: 2018-11-05 12:00:00+00:00, Price: 122.3961 Timestamp: 2018-11-05 12:30:00+00:00, Price: 122.4855 Timestamp: 2018-11-05 12:45:00+00:00, Price: 122.4855 Timestamp: 2018-11-05 13:00:00+00:00, Price: 122.3067 Timestamp: 2018-11-05 13:15:00+00:00, Price: 122.3067 Timestamp: 2018-11-05 13:30:00+00:00, Price: 123.1113 Timestamp: 2018-11-05 14:30:00+00:00, Price: 122.4855 Timestamp: 2018-11-05 14:45:00+00:00, Price: 122.5749 Timestamp: 2018-11-05 15:00:00+00:00, Price: 123.2901 Timestamp: 2018-11-06 06:45:00+00:00, Price: 123.2901 Timestamp: 2018-11-06 07:00:00+00:00, Price: 122.3067 Timestamp: 2018-11-06 08:30:00+00:00, Price: 122.3961 Timestamp: 2018-11-07 11:30:00+00:00, Price: 122.3067 Timestamp: 2018-11-07 12:15:00+00:00, Price: 123.1113 Timestamp: 2018-11-07 12:30:00+00:00, Price: 123.3795 Timestamp: 2018-11-07 12:45:00+00:00, Price: 123.5583 Timestamp: 2018-11-07 13:00:00+00:00, Price: 123.5583 Timestamp: 2018-11-07 13:15:00+00:00, Price: 123.3795 Timestamp: 2018-11-07 13:30:00+00:00, Price: 123.3795 Timestamp: 2018-11-07 13:45:00+00:00, Price: 123.3795 Timestamp: 2018-11-07 14:00:00+00:00, Price: 123.6477 Timestamp: 2018-11-07 14:15:00+00:00, Price: 123.5583 Timestamp: 2018-11-07 14:30:00+00:00, Price: 123.7371 Timestamp: 2018-11-07 14:45:00+00:00, Price: 123.2901 Timestamp: 2018-11-07 15:00:00+00:00, Price: 122.9325
Upper Outliers for TCELL in 2018/11 Timestamp: 2018-11-28 12:45:00+00:00, Price: 12.6381 Timestamp: 2018-11-28 13:00:00+00:00, Price: 12.6869 Timestamp: 2018-11-28 13:15:00+00:00, Price: 12.7064 Timestamp: 2018-11-28 13:30:00+00:00, Price: 12.6967 Timestamp: 2018-11-28 13:45:00+00:00, Price: 12.6576 Timestamp: 2018-11-29 07:15:00+00:00, Price: 12.7064 Timestamp: 2018-11-29 07:30:00+00:00, Price: 12.6967 Timestamp: 2018-11-29 07:45:00+00:00, Price: 12.6869 Timestamp: 2018-11-29 08:00:00+00:00, Price: 12.6381 Timestamp: 2018-11-29 09:15:00+00:00, Price: 12.6674 Timestamp: 2018-11-29 09:30:00+00:00, Price: 12.6478 Timestamp: 2018-11-29 11:00:00+00:00, Price: 12.6381 Timestamp: 2018-11-29 11:15:00+00:00, Price: 12.6478 Timestamp: 2018-11-29 11:30:00+00:00, Price: 12.6478 Timestamp: 2018-11-29 11:45:00+00:00, Price: 12.6381 Timestamp: 2018-11-29 12:00:00+00:00, Price: 12.6381 Timestamp: 2018-11-29 12:15:00+00:00, Price: 12.6478 Timestamp: 2018-11-29 12:30:00+00:00, Price: 12.6576 Timestamp: 2018-11-29 12:45:00+00:00, Price: 12.6576
NO OUTLIERS
Upper Outliers for AKBNK in 2018/12 Timestamp: 2018-12-03 06:45:00+00:00, Price: 6.6831 Timestamp: 2018-12-03 07:00:00+00:00, Price: 6.7003 Timestamp: 2018-12-03 07:15:00+00:00, Price: 6.6831 Timestamp: 2018-12-03 07:30:00+00:00, Price: 6.6917 Timestamp: 2018-12-03 07:45:00+00:00, Price: 6.6746 Timestamp: 2018-12-03 08:00:00+00:00, Price: 6.7089 Timestamp: 2018-12-03 08:15:00+00:00, Price: 6.7175 Timestamp: 2018-12-03 08:30:00+00:00, Price: 6.6746 Timestamp: 2018-12-03 08:45:00+00:00, Price: 6.6574 Timestamp: 2018-12-03 09:00:00+00:00, Price: 6.6402 Timestamp: 2018-12-03 09:15:00+00:00, Price: 6.6488 Timestamp: 2018-12-03 09:30:00+00:00, Price: 6.6574 Timestamp: 2018-12-03 09:45:00+00:00, Price: 6.6488 Timestamp: 2018-12-03 10:45:00+00:00, Price: 6.6317 Timestamp: 2018-12-03 11:00:00+00:00, Price: 6.5973 Timestamp: 2018-12-03 11:15:00+00:00, Price: 6.5802 Timestamp: 2018-12-03 11:30:00+00:00, Price: 6.563 Timestamp: 2018-12-03 11:45:00+00:00, Price: 6.5545 Timestamp: 2018-12-03 12:00:00+00:00, Price: 6.5373 Timestamp: 2018-12-03 12:15:00+00:00, Price: 6.563 Timestamp: 2018-12-03 12:30:00+00:00, Price: 6.5545 Timestamp: 2018-12-03 12:45:00+00:00, Price: 6.5201 Timestamp: 2018-12-03 13:00:00+00:00, Price: 6.5459 Timestamp: 2018-12-03 13:15:00+00:00, Price: 6.5716 Timestamp: 2018-12-03 13:30:00+00:00, Price: 6.563 Timestamp: 2018-12-03 13:45:00+00:00, Price: 6.5459 Timestamp: 2018-12-03 14:00:00+00:00, Price: 6.5716 Timestamp: 2018-12-03 14:15:00+00:00, Price: 6.5287 Timestamp: 2018-12-03 14:30:00+00:00, Price: 6.503 Timestamp: 2018-12-03 14:45:00+00:00, Price: 6.503 Timestamp: 2018-12-03 15:00:00+00:00, Price: 6.4944 Timestamp: 2018-12-04 06:45:00+00:00, Price: 6.4687 Timestamp: 2018-12-04 07:00:00+00:00, Price: 6.5116 Timestamp: 2018-12-04 07:15:00+00:00, Price: 6.5116 Timestamp: 2018-12-04 07:30:00+00:00, Price: 6.4944 Timestamp: 2018-12-04 07:45:00+00:00, Price: 6.4944 Timestamp: 2018-12-04 08:00:00+00:00, Price: 6.4601 Timestamp: 2018-12-04 08:15:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 08:30:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 08:45:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 09:00:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 09:15:00+00:00, Price: 6.4429 Timestamp: 2018-12-04 09:30:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 09:45:00+00:00, Price: 6.4687 Timestamp: 2018-12-04 10:00:00+00:00, Price: 6.4643999999999995 Timestamp: 2018-12-04 10:45:00+00:00, Price: 6.4601 Timestamp: 2018-12-04 11:00:00+00:00, Price: 6.4601 Timestamp: 2018-12-04 11:15:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 11:30:00+00:00, Price: 6.4515 Timestamp: 2018-12-04 11:45:00+00:00, Price: 6.4601 Timestamp: 2018-12-04 12:00:00+00:00, Price: 6.4601 Timestamp: 2018-12-04 12:15:00+00:00, Price: 6.4429 Timestamp: 2018-12-04 12:30:00+00:00, Price: 6.4343 Timestamp: 2018-12-04 12:45:00+00:00, Price: 6.4258 Timestamp: 2018-12-04 13:00:00+00:00, Price: 6.4086 Timestamp: 2018-12-04 13:15:00+00:00, Price: 6.4086 Timestamp: 2018-12-04 13:30:00+00:00, Price: 6.3914 Timestamp: 2018-12-04 13:45:00+00:00, Price: 6.34 Timestamp: 2018-12-04 14:00:00+00:00, Price: 6.3228 Timestamp: 2018-12-04 14:15:00+00:00, Price: 6.3057 Timestamp: 2018-12-04 14:30:00+00:00, Price: 6.3228 Timestamp: 2018-12-04 14:45:00+00:00, Price: 6.2799 Timestamp: 2018-12-04 15:00:00+00:00, Price: 6.2885 Lower Outliers for AKBNK in 2018/12 Timestamp: 2018-12-14 08:45:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 09:00:00+00:00, Price: 5.422 Timestamp: 2018-12-14 09:15:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 10:45:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 11:00:00+00:00, Price: 5.4048 Timestamp: 2018-12-14 11:30:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 11:45:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 12:15:00+00:00, Price: 5.422 Timestamp: 2018-12-14 12:30:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 12:45:00+00:00, Price: 5.4134 Timestamp: 2018-12-14 13:00:00+00:00, Price: 5.4134 Timestamp: 2018-12-14 13:15:00+00:00, Price: 5.3963 Timestamp: 2018-12-14 13:30:00+00:00, Price: 5.4048 Timestamp: 2018-12-14 13:45:00+00:00, Price: 5.422 Timestamp: 2018-12-14 14:00:00+00:00, Price: 5.422 Timestamp: 2018-12-14 14:15:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 14:30:00+00:00, Price: 5.4306 Timestamp: 2018-12-14 14:45:00+00:00, Price: 5.422
Upper Outliers for VAKBN in 2018/12 Timestamp: 2018-12-03 06:45:00+00:00, Price: 4.1 Timestamp: 2018-12-03 07:00:00+00:00, Price: 4.06 Timestamp: 2018-12-03 07:15:00+00:00, Price: 4.08 Timestamp: 2018-12-03 07:30:00+00:00, Price: 4.11 Timestamp: 2018-12-03 07:45:00+00:00, Price: 4.1 Timestamp: 2018-12-03 08:00:00+00:00, Price: 4.1 Timestamp: 2018-12-03 08:15:00+00:00, Price: 4.09 Timestamp: 2018-12-03 08:30:00+00:00, Price: 4.08 Timestamp: 2018-12-03 08:45:00+00:00, Price: 4.07 Timestamp: 2018-12-03 09:00:00+00:00, Price: 4.07 Timestamp: 2018-12-03 09:15:00+00:00, Price: 4.08 Timestamp: 2018-12-03 09:30:00+00:00, Price: 4.09 Timestamp: 2018-12-03 09:45:00+00:00, Price: 4.08 Timestamp: 2018-12-03 10:45:00+00:00, Price: 4.07 Timestamp: 2018-12-03 11:00:00+00:00, Price: 4.07 Timestamp: 2018-12-03 11:15:00+00:00, Price: 4.07 Timestamp: 2018-12-03 11:30:00+00:00, Price: 4.06 Timestamp: 2018-12-03 11:45:00+00:00, Price: 4.06 Timestamp: 2018-12-03 12:15:00+00:00, Price: 4.07 Timestamp: 2018-12-03 13:00:00+00:00, Price: 4.06 Timestamp: 2018-12-03 13:15:00+00:00, Price: 4.06 Timestamp: 2018-12-03 13:30:00+00:00, Price: 4.09 Timestamp: 2018-12-03 13:45:00+00:00, Price: 4.07 Timestamp: 2018-12-03 14:00:00+00:00, Price: 4.08 Timestamp: 2018-12-03 14:15:00+00:00, Price: 4.06 Lower Outliers for VAKBN in 2018/12 Timestamp: 2018-12-17 15:00:00+00:00, Price: 3.69 Timestamp: 2018-12-18 06:45:00+00:00, Price: 3.67
Upper Outliers for ARCLK in 2018/12 Timestamp: 2018-12-03 08:15:00+00:00, Price: 15.9 Lower Outliers for ARCLK in 2018/12 Timestamp: 2018-12-12 13:15:00+00:00, Price: 15.1
NO OUTLIERS
NO OUTLIERS
Upper Outliers for THYAO in 2018/12 Timestamp: 2018-12-03 06:45:00+00:00, Price: 17.11 Timestamp: 2018-12-03 07:00:00+00:00, Price: 17.06 Timestamp: 2018-12-03 07:15:00+00:00, Price: 17.07 Timestamp: 2018-12-03 07:30:00+00:00, Price: 17.12 Timestamp: 2018-12-03 07:45:00+00:00, Price: 17.1 Timestamp: 2018-12-03 08:00:00+00:00, Price: 17.08 Timestamp: 2018-12-03 08:15:00+00:00, Price: 17.03 Lower Outliers for THYAO in 2018/12 Timestamp: 2018-12-14 08:15:00+00:00, Price: 14.93 Timestamp: 2018-12-14 08:30:00+00:00, Price: 14.89 Timestamp: 2018-12-14 08:45:00+00:00, Price: 14.88 Timestamp: 2018-12-14 09:00:00+00:00, Price: 14.9 Timestamp: 2018-12-14 09:15:00+00:00, Price: 14.91 Timestamp: 2018-12-14 09:30:00+00:00, Price: 14.84 Timestamp: 2018-12-14 09:45:00+00:00, Price: 14.82 Timestamp: 2018-12-14 10:00:00+00:00, Price: 14.815000000000001 Timestamp: 2018-12-14 10:45:00+00:00, Price: 14.81 Timestamp: 2018-12-14 11:00:00+00:00, Price: 14.79 Timestamp: 2018-12-14 11:15:00+00:00, Price: 14.85 Timestamp: 2018-12-14 11:30:00+00:00, Price: 14.82 Timestamp: 2018-12-14 11:45:00+00:00, Price: 14.85 Timestamp: 2018-12-14 12:00:00+00:00, Price: 14.84 Timestamp: 2018-12-14 12:15:00+00:00, Price: 14.89 Timestamp: 2018-12-14 12:30:00+00:00, Price: 14.9 Timestamp: 2018-12-14 12:45:00+00:00, Price: 14.84 Timestamp: 2018-12-14 13:00:00+00:00, Price: 14.83 Timestamp: 2018-12-14 13:15:00+00:00, Price: 14.78 Timestamp: 2018-12-14 13:30:00+00:00, Price: 14.82 Timestamp: 2018-12-14 13:45:00+00:00, Price: 14.86 Timestamp: 2018-12-14 14:00:00+00:00, Price: 14.88 Timestamp: 2018-12-14 14:15:00+00:00, Price: 14.89 Timestamp: 2018-12-14 14:30:00+00:00, Price: 14.89 Timestamp: 2018-12-14 14:45:00+00:00, Price: 14.9 Timestamp: 2018-12-14 15:00:00+00:00, Price: 14.96 Timestamp: 2018-12-17 06:45:00+00:00, Price: 15.03 Timestamp: 2018-12-17 07:00:00+00:00, Price: 14.98 Timestamp: 2018-12-17 07:15:00+00:00, Price: 14.92 Timestamp: 2018-12-17 07:30:00+00:00, Price: 14.9 Timestamp: 2018-12-17 07:45:00+00:00, Price: 14.94 Timestamp: 2018-12-17 08:00:00+00:00, Price: 14.93 Timestamp: 2018-12-17 08:15:00+00:00, Price: 15.01 Timestamp: 2018-12-17 08:30:00+00:00, Price: 15.0 Timestamp: 2018-12-17 08:45:00+00:00, Price: 15.02 Timestamp: 2018-12-17 09:00:00+00:00, Price: 14.98 Timestamp: 2018-12-17 09:15:00+00:00, Price: 15.03 Timestamp: 2018-12-17 09:30:00+00:00, Price: 15.05 Timestamp: 2018-12-17 09:45:00+00:00, Price: 15.0 Timestamp: 2018-12-17 10:00:00+00:00, Price: 15.004999999999999 Timestamp: 2018-12-17 10:45:00+00:00, Price: 15.01 Timestamp: 2018-12-17 11:00:00+00:00, Price: 15.01 Timestamp: 2018-12-17 11:15:00+00:00, Price: 14.96 Timestamp: 2018-12-17 11:30:00+00:00, Price: 14.85 Timestamp: 2018-12-17 11:45:00+00:00, Price: 14.9 Timestamp: 2018-12-17 12:00:00+00:00, Price: 14.9 Timestamp: 2018-12-17 12:15:00+00:00, Price: 14.91 Timestamp: 2018-12-17 12:30:00+00:00, Price: 14.9 Timestamp: 2018-12-17 12:45:00+00:00, Price: 14.86 Timestamp: 2018-12-17 13:00:00+00:00, Price: 14.83 Timestamp: 2018-12-17 13:15:00+00:00, Price: 14.86 Timestamp: 2018-12-17 13:30:00+00:00, Price: 14.84 Timestamp: 2018-12-17 13:45:00+00:00, Price: 14.82 Timestamp: 2018-12-17 14:00:00+00:00, Price: 14.84 Timestamp: 2018-12-17 14:15:00+00:00, Price: 14.84 Timestamp: 2018-12-17 14:30:00+00:00, Price: 14.74 Timestamp: 2018-12-17 14:45:00+00:00, Price: 14.75 Timestamp: 2018-12-17 15:00:00+00:00, Price: 14.69 Timestamp: 2018-12-18 06:45:00+00:00, Price: 14.69 Timestamp: 2018-12-18 07:00:00+00:00, Price: 14.79 Timestamp: 2018-12-18 07:15:00+00:00, Price: 14.83 Timestamp: 2018-12-18 07:30:00+00:00, Price: 14.85 Timestamp: 2018-12-18 07:45:00+00:00, Price: 14.83 Timestamp: 2018-12-18 08:00:00+00:00, Price: 14.83 Timestamp: 2018-12-18 08:15:00+00:00, Price: 14.83 Timestamp: 2018-12-18 08:30:00+00:00, Price: 14.82 Timestamp: 2018-12-18 08:45:00+00:00, Price: 14.83 Timestamp: 2018-12-18 09:00:00+00:00, Price: 14.84 Timestamp: 2018-12-18 09:15:00+00:00, Price: 14.84 Timestamp: 2018-12-18 09:30:00+00:00, Price: 14.87 Timestamp: 2018-12-18 09:45:00+00:00, Price: 15.0 Timestamp: 2018-12-18 10:00:00+00:00, Price: 15.0 Timestamp: 2018-12-18 10:45:00+00:00, Price: 15.02 Timestamp: 2018-12-18 11:00:00+00:00, Price: 15.01 Timestamp: 2018-12-18 11:15:00+00:00, Price: 14.99 Timestamp: 2018-12-18 11:30:00+00:00, Price: 15.03 Timestamp: 2018-12-18 11:45:00+00:00, Price: 15.03 Timestamp: 2018-12-18 12:00:00+00:00, Price: 14.96 Timestamp: 2018-12-18 12:15:00+00:00, Price: 14.97 Timestamp: 2018-12-18 12:30:00+00:00, Price: 15.08 Timestamp: 2018-12-18 12:45:00+00:00, Price: 15.08
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
NO OUTLIERS
Upper Outliers for THYAO in 2019/1 Timestamp: 2019-01-02 06:45:00+00:00, Price: 16.13 Timestamp: 2019-01-02 07:00:00+00:00, Price: 16.15 Timestamp: 2019-01-02 07:15:00+00:00, Price: 16.17 Timestamp: 2019-01-02 07:30:00+00:00, Price: 16.23 Timestamp: 2019-01-02 07:45:00+00:00, Price: 16.24 Timestamp: 2019-01-02 08:00:00+00:00, Price: 16.15 Timestamp: 2019-01-02 08:15:00+00:00, Price: 16.1 Timestamp: 2019-01-02 08:30:00+00:00, Price: 16.14 Timestamp: 2019-01-02 08:45:00+00:00, Price: 16.12 Timestamp: 2019-01-02 09:00:00+00:00, Price: 16.15 Timestamp: 2019-01-02 09:15:00+00:00, Price: 16.17 Timestamp: 2019-01-02 09:30:00+00:00, Price: 16.04 Timestamp: 2019-01-02 09:45:00+00:00, Price: 16.03 Timestamp: 2019-01-02 10:00:00+00:00, Price: 16.025 Timestamp: 2019-01-02 10:45:00+00:00, Price: 16.02 Timestamp: 2019-01-02 11:00:00+00:00, Price: 15.97 Timestamp: 2019-01-02 11:15:00+00:00, Price: 15.96 Timestamp: 2019-01-02 11:30:00+00:00, Price: 15.93 Timestamp: 2019-01-02 11:45:00+00:00, Price: 15.95 Timestamp: 2019-01-02 12:00:00+00:00, Price: 15.98 Timestamp: 2019-01-02 12:15:00+00:00, Price: 15.95 Timestamp: 2019-01-02 12:30:00+00:00, Price: 15.95 Timestamp: 2019-01-02 12:45:00+00:00, Price: 15.96 Timestamp: 2019-01-02 13:00:00+00:00, Price: 15.97 Timestamp: 2019-01-02 13:15:00+00:00, Price: 15.86 Timestamp: 2019-01-02 13:30:00+00:00, Price: 15.73 Timestamp: 2019-01-02 13:45:00+00:00, Price: 15.77 Timestamp: 2019-01-02 14:00:00+00:00, Price: 15.75 Timestamp: 2019-01-02 14:30:00+00:00, Price: 15.7 Timestamp: 2019-01-02 14:45:00+00:00, Price: 15.68 Timestamp: 2019-01-02 15:00:00+00:00, Price: 15.72 Timestamp: 2019-01-03 07:00:00+00:00, Price: 15.76 Timestamp: 2019-01-03 07:15:00+00:00, Price: 15.79 Timestamp: 2019-01-03 07:30:00+00:00, Price: 15.74 Timestamp: 2019-01-03 07:45:00+00:00, Price: 15.68 Timestamp: 2019-01-03 08:00:00+00:00, Price: 15.77 Timestamp: 2019-01-03 08:15:00+00:00, Price: 15.83 Timestamp: 2019-01-03 08:30:00+00:00, Price: 15.95 Timestamp: 2019-01-03 08:45:00+00:00, Price: 15.85 Timestamp: 2019-01-03 09:00:00+00:00, Price: 15.93 Timestamp: 2019-01-03 09:15:00+00:00, Price: 15.89 Timestamp: 2019-01-03 09:30:00+00:00, Price: 15.82 Timestamp: 2019-01-03 09:45:00+00:00, Price: 15.79 Timestamp: 2019-01-03 10:00:00+00:00, Price: 15.8 Timestamp: 2019-01-03 10:45:00+00:00, Price: 15.81 Timestamp: 2019-01-03 11:00:00+00:00, Price: 15.79 Timestamp: 2019-01-03 11:15:00+00:00, Price: 15.81 Timestamp: 2019-01-03 11:30:00+00:00, Price: 15.73 Timestamp: 2019-01-03 11:45:00+00:00, Price: 15.74 Timestamp: 2019-01-03 12:00:00+00:00, Price: 15.71 Timestamp: 2019-01-03 12:15:00+00:00, Price: 15.7 Timestamp: 2019-01-03 12:30:00+00:00, Price: 15.71 Timestamp: 2019-01-03 13:15:00+00:00, Price: 15.68
In this part we will use the data we obtained from Google Trends for the stocks that we are interested in and make a line of search volumes and stock prices to identify the causes.
# returns outliers obtained from control charts in part 3
def get_outliers(stock_name):
if stock_name == "AKBNK":
return outliers_akbnk
elif stock_name == "VAKBN":
return outliers_vakbn
elif stock_name == "ARCLK":
return outliers_arclk
elif stock_name == "TUPRS":
return outliers_tuprs
elif stock_name == "TCELL":
return outliers_tcell
else:
return outliers_thyao
'''
Below function plots the quarterly trend, stock prices, and outliers
given the stock name.
'''
def plot_trend(stock_name):
df_trend = pd.read_csv(f"{stock_name}.csv", header=0)
df_trend["Week"] = pd.to_datetime(df_trend["Week"])
# start and end dates
# notice that we observe them quarterly
# but the outlier data we used is obtained from part 3, therefore monthly
start = pd.to_datetime("2017-01-15")
end = start + pd.DateOffset(months=3)
# initialize the figure
fig, axes = plt.subplots(4, 2, figsize=(22, 22))
fig.subplots_adjust(hspace=0.5)
# we have 8 quarters (2 years)
for i in range(8):
# get the outliers for quarter
outliers = get_outliers(stock_name)
outliers_quarter = [i for i in outliers if i[0] >= start.tz_localize('UTC') and i[0] <= end.tz_localize('UTC')]
outliers_quarter = pd.DataFrame(outliers_quarter, columns=['timestamp', 'value'])
# copy df
df = filled_df.copy()
# filtering with time and adding UTC timezone
ndf = df[(df['timestamp'] >= start.tz_localize('UTC')) & (df['timestamp'] <= end.tz_localize('UTC'))]
# filtering the necessary columns
df = ndf[['timestamp', 'year', 'month', 'day', stock_name]].copy()
# We must scale both of the df between 0-1 since their scale is very different
scaler = MinMaxScaler(feature_range=(0, 1))
df[stock_name] = scaler.fit_transform(df[stock_name].values.reshape(-1, 1))
# scale outliers
if len(outliers_quarter) > 0:
outliers_quarter["value"] = scaler.transform(outliers_quarter["value"].values.reshape(-1, 1))
# filtering df_akbnk with the same timezone
df_trend_filtered = df_trend[(df_trend['Week'] >= start) & (df_trend['Week'] <= end)]
# min max scaler
scaler = MinMaxScaler(feature_range=(0, 1))
df_trend_filtered[f"IST:{stock_name}: (Türkiye)"] = scaler.fit_transform(df_trend_filtered[f"IST:{stock_name}: (Türkiye)"].values.reshape(-1, 1))
row = i // 2
col = i % 2
# creating a figure and axis
ax = axes[row, col]
# plotting the data from df_akbnk_filtered
ax.plot(df_trend_filtered["Week"], df_trend_filtered[f"IST:{stock_name}: (Türkiye)"], label="Trend Data", color="green", linewidth=3)
# plotting the data from df_copy (assuming "timestamp" is your x-axis)
ax.plot(df["timestamp"], df[stock_name], label="Stock Price", color="orangered")
# plot outliers as red dots
if len(outliers_quarter) > 0:
ax.scatter(outliers_quarter['timestamp'], outliers_quarter['value'], c='red', marker='o', s=100, label='Outliers')
# x, y axis, title and legend
ax.set_xlabel(f"Q{i % 4 + 1} {start.year}", fontsize=16)
ax.set_ylabel("Stock Prices and \nSearch Volume \n(Normalized 0-1)", fontsize=16)
ax.grid(True)
ax.set_title(f"{stock_name}\nTrend Data vs. Stock Price", fontsize=18, fontweight="bold")
ax.legend(loc="upper left")
# off-setting dates
start = end
end = start + relativedelta(months=3)
plt.show()
plot_trend("AKBNK")
plot_trend("VAKBN")
plot_trend("ARCLK")
plot_trend("TUPRS")
plot_trend("TCELL")
plot_trend("THYAO")